Central repository for storing configuration files of a distributed computer system

ABSTRACT

In a computer-implemented method for configuring a distributed computer system comprising a plurality of nodes of a plurality of node classes, configuration files for a plurality of nodes of each of the plurality of node classes are stored in a central repository. The configuration files include information representing a desired system state of the distributed computer system, and the distributed computer system operates to keep an actual system state of the distributed computer system consistent with the desired system state. The plurality of node classes includes forwarder nodes for receiving data from an input source, indexer nodes for indexing the data, and search head nodes for searching the data. Responsive to receiving changes to the configuration files, the changes are propagated to nodes of the plurality of nodes impacted by the changes based on a node class of the nodes impacted by the changes.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of U.S patent application Ser. No.15/143,472, filed Apr. 29, 2016 and entitled “CENTRAL REPOSITORY FORSTORING CONFIGURATION FILES OF A DISTRIBUTED COMPUTER SYSTEM,” theentire contents of which are incorporated by reference herein.

BACKGROUND

Modern data centers often comprise thousands of hosts that operatecollectively to service requests from even larger numbers of remoteclients. During operation, components of these data centers can producesignificant volumes of machine-generated data. The unstructured natureof much of this data has made it challenging to perform indexing andsearching operations because of the difficulty of applying semanticmeaning to unstructured data. As the number of hosts and clientsassociated with a data center continues to grow, processing largevolumes of machine-generated data in an intelligent manner andeffectively presenting the results of such processing continues to be apriority.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and form a part ofthe Description of Embodiments, illustrate various embodiments of thesubject matter and, together with the Description of Embodiments, serveto explain principles of the subject matter discussed below. Unlessspecifically noted, the drawings referred to in this Brief Descriptionof Drawings should be understood as not being drawn to scale. Herein,like items are labeled with like item numbers:

FIG. 1 illustrates a networked computer environment in which anembodiment may be implemented;

FIG. 2 illustrates a block diagram of an example data intake and querysystem in which an embodiment may be implemented;

FIG. 3 is a flow diagram that illustrates how indexers process, index,and store data received from forwarders in accordance with the disclosedembodiments;

FIG. 4 is a flow diagram that illustrates how a search head and indexersperform a search query in accordance with the disclosed embodiments;

FIG. 5 illustrates a scenario where a common customer ID is found amonglog data received from three disparate sources in accordance with thedisclosed embodiments;

FIG. 6A illustrates a search screen in accordance with the disclosedembodiments;

FIG. 6B illustrates a data summary dialog that enables a user to selectvarious data sources in accordance with the disclosed embodiments;

FIGS. 7A-7D illustrate a series of user interface screens for an exampledata model-driven report generation interface in accordance with thedisclosed embodiments;

FIG. 8 illustrates an example search query received from a client andexecuted by search peers in accordance with the disclosed embodiments;

FIG. 9A illustrates a key indicators view in accordance with thedisclosed embodiments;

FIG. 9B illustrates an incident review dashboard in accordance with thedisclosed embodiments;

FIG. 9C illustrates a proactive monitoring tree in accordance with thedisclosed embodiments;

FIG. 9D illustrates a user interface screen displaying both log data andperformance data in accordance with the disclosed embodiments;

FIG. 10 illustrates a block diagram of an example cloud-based dataintake and query system in which an embodiment may be implemented;

FIG. 11 illustrates a block diagram of an example data intake and querysystem that performs searches across external data systems in accordancewith the disclosed embodiments;

FIGS. 12-14 illustrate a series of user interface screens for an exampledata model-driven report generation interface in accordance with thedisclosed embodiments;

FIGS. 15-17 illustrate example visualizations generated by a reportingapplication in accordance with the disclosed embodiments;

FIG. 18A illustrates a block diagram of an example distributed computersystem for maintaining a central repository of configuration filesacross nodes of different node classes (e.g., search head nodes, indexernodes, and forwarder nodes), in accordance with some embodiments;

FIG. 18B illustrates a block diagram of an example organization of acentral repository of configuration files across nodes of different nodeclasses, in accordance with some embodiments;

FIG. 19 illustrates a data flow diagram of the propagation ofconfiguration files between a central repository of configuration filesand nodes of the example distributed computer system, in accordance withsome embodiments;

FIG. 20 illustrates a data flow diagram of the asynchronous feedback ofthe configuration status of nodes of the example distributed computersystem, in accordance with some embodiments;

FIG. 21 illustrates a data flow diagram of the maintenance andpropagation of configuration files to specific nodes (e.g., snowflakenodes) of the example distributed computer system, in accordance withsome embodiments;

FIG. 22 illustrates a data flow diagram of an example propagation ofconfiguration files to forwarder nodes, in accordance with someembodiments;

FIG. 23 illustrates an example flow diagram for committing configurationchanges to the central repository, and for resolving conflicts, inaccordance with various embodiments;

FIGS. 24-27 illustrate processes for configuring a distributed computersystem comprising a plurality of nodes of a plurality of node classes,in accordance with some embodiments;

FIGS. 28A-28D illustrate a series of user interface screens for anexample node configuration user interface, in accordance with the someembodiments; and

FIG. 29 illustrate processes for configuring a distributed computersystem comprising a plurality of nodes of a plurality of node classes ata user interface, in accordance with some embodiments.

DETAILED DESCRIPTION

Reference will now be made in detail to various embodiments of thesubject matter, examples of which are illustrated in the accompanyingdrawings. While various embodiments are discussed herein, it will beunderstood that they are not intended to limit to these embodiments. Onthe contrary, the presented embodiments are intended to coveralternatives, modifications and equivalents, which may be includedwithin the spirit and scope the various embodiments as defined by theappended claims. Furthermore, in this description of embodiments,numerous specific details are set forth in order to provide a thoroughunderstanding of embodiments of the present subject matter. However,embodiments may be practiced without these specific details. In otherinstances, well known methods, procedures, components, and circuits havenot been described in detail as not to unnecessarily obscure aspects ofthe described embodiments.

The present disclosure is directed to centralizing configurations of adistributed system, such as a data aggregation and analysis system, at acentral repository. The distributed system includes nodes of multiplenode classes, including forwarders, indexers and search heads. Aforwarder may refer to a component of the data aggregation and analysissystem that is responsible for collecting data from a variety of datasources. An indexer may refer to a component of the data aggregation andanalysis system that is responsible for storing, processing and/orperforming operations on the collected data. A search head may refer toa component of the data aggregation and analysis system that isresponsible for performing search operations on the collected data.Nodes, and/or clusters of nodes, of the data aggregation and analysissystem can be customized, with the customization being stored inconfiguration files. Embodiments described herein provide a centralrepository for storing and maintaining the configuration files of thedistributed system.

Various embodiments described herein provide a central repository thatmaintains configuration files in a desired system state and thedistributed system operates to keep the actual system state consistentwith the desired system state. For some node classes, such as searchheads, synchronization between the nodes and the central repository isbi-directional, as changes can be made to the configuration files at thenodes themselves or at the central repository. For other node classes,such as indexers and forwarders, the synchronization is one-directionalfrom the central repository to the nodes due to the central managementof the configuration files for these node classes. Administrativefunctions can be performed via a management node that maintains thecentral repository of the desired system state of the configurationfiles.

Notation and Nomenclature

Some portions of the detailed descriptions which follow are presented interms of procedures, logic blocks, processing and other symbolicrepresentations of operations on data bits within a computer memory.These descriptions and representations are the means used by thoseskilled in the data processing arts to most effectively convey thesubstance of their work to others skilled in the art. In the presentapplication, a procedure, logic block, process, or the like, isconceived to be one or more self-consistent procedures or instructionsleading to a desired result. The procedures are those requiring physicalmanipulations of physical quantities. Usually, although not necessarily,these quantities take the form of electrical or magnetic signals capableof being stored, transferred, combined, compared, and otherwisemanipulated in an electronic device.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the followingdiscussions, it is appreciated that throughout the description ofembodiments, discussions utilizing terms such as “storing,” “receiving,”“propagating,” “providing,” “comparing,” “determining,” “merging,”“searching,” “configuring,” or the like, refer to the actions andprocesses of an electronic device such as: a host processor, aprocessor, a processing unit, a computer system, a networked computersystem, or the like, or a combination thereof. The electronic devicemanipulates and transforms data represented as physical (electronicand/or magnetic) quantities within the electronic device's registers andmemories into other data similarly represented as physical quantitieswithin the electronic device's memories or registers or other suchinformation storage, transmission, processing, or display components.

Embodiments described herein may be discussed in the general context ofprocessor-executable instructions residing on some form ofnon-transitory processor-readable medium, such as program modules,executed by one or more computers or other devices. Generally, programmodules include routines, programs, objects, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. The functionality of the program modules may becombined or distributed as desired in various embodiments.

In the figures, a single block may be described as performing a functionor functions; however, in actual practice, the function or functionsperformed by that block may be performed in a single component or acrossmultiple components, and/or may be performed using hardware, usingsoftware, or using a combination of hardware and software. To clearlyillustrate this interchangeability of hardware and software, variousillustrative components, blocks, modules, circuits, and steps have beendescribed generally in terms of their functionality. Whether suchfunctionality is implemented as hardware or software depends upon theparticular application and design constraints imposed on the overallsystem. Skilled artisans may implement the described functionality invarying ways for each particular application, but such implementationdecisions should not be interpreted as causing a departure from thescope of the present disclosure. Also, the example mobile electronicdevice described herein may include components other than those shown,including well-known components.

The techniques described herein may be implemented in hardware,software, firmware, or any combination thereof, unless specificallydescribed as being implemented in a specific manner. Any featuresdescribed as modules or components may also be implemented together inan integrated logic device or separately as discrete but interoperablelogic devices. If implemented in software, the techniques may berealized at least in part by a non-transitory processor-readable storagemedium comprising instructions that, when executed, perform one or moreof the methods described herein. The non-transitory processor-readabledata storage medium may form part of a computer program product, whichmay include packaging materials.

The non-transitory processor-readable storage medium may comprise randomaccess memory (RAM) such as synchronous dynamic random access memory(SDRAM), read only memory (ROM), non-volatile random access memory(NVRAM), electrically erasable programmable read-only memory (EEPROM),FLASH memory, other known storage media, and the like. The techniquesadditionally, or alternatively, may be realized at least in part by aprocessor-readable communication medium that carries or communicatescode in the form of instructions or data structures and that can beaccessed, read, and/or executed by a computer or other processor.

The various illustrative logical blocks, modules, circuits andinstructions described in connection with the embodiments disclosedherein may be executed by one or more processors, such as one or moremotion processing units (MPUs), sensor processing units (SPUs), hostprocessor(s) or core(s) thereof, digital signal processors (DSPs),general purpose microprocessors, application specific integratedcircuits (ASICs), application specific instruction set processors(ASIPs), field programmable gate arrays (FPGAs), or other equivalentintegrated or discrete logic circuitry. The term “processor,” as usedherein may refer to any of the foregoing structures or any otherstructure suitable for implementation of the techniques describedherein. In addition, in some aspects, the functionality described hereinmay be provided within dedicated software modules or hardware modulesconfigured as described herein. Also, the techniques could be fullyimplemented in one or more circuits or logic elements. A general purposeprocessor may be a microprocessor, but in the alternative, the processormay be any conventional processor, controller, microcontroller, or statemachine. A processor may also be implemented as a combination ofcomputing devices, e.g., a combination of an SPU/MPU and amicroprocessor, a plurality of microprocessors, one or moremicroprocessors in conjunction with an SPU core, MPU core, or any othersuch configuration.

Embodiments are described herein according to the following outline:

-   -   1.0. General Overview    -   2.0. Operating Environment        -   2.1. Host Devices        -   2.2. Client Devices        -   2.3. Client Device Applications        -   2.4. Data Server System        -   2.5. Data Ingestion            -   2.5.1. Input            -   2.5.2. Parsing            -   2.5.3. Indexing        -   2.6. Query Processing        -   2.7. Field Extraction        -   2.8. Example Search Screen        -   2.9. Data Modelling        -   2.10. Acceleration Techniques            -   2.10.1. Aggregation Technique            -   2.10.2. Keyword Index            -   2.10.3. High Performance Analytics Store            -   2.10.4. Accelerating Report Generation        -   2.11. Data Center Monitoring        -   2.12. Cloud-Based System Overview        -   2.13. Searching Externally Archived Data        -   2.13.1. ERP Process Features    -   3.0. Central Repository for Configuration Files        -   3.1 Example Processes of Operation

1.0. General Overview

Modern data centers and other computing environments can compriseanywhere from a few host computer systems to thousands of systemsconfigured to process data, service requests from remote clients, andperform numerous other computational tasks. During operation, variouscomponents within these computing environments often generatesignificant volumes of machine-generated data. For example, machine datais generated by various components in the information technology (IT)environments, such as servers, sensors, routers, mobile devices,Internet of Things (IoT) devices, etc. Machine-generated data caninclude system logs, network packet data, sensor data, applicationprogram data, error logs, stack traces, system performance data, etc. Ingeneral, machine-generated data can also include performance data,diagnostic information, and many other types of data that can beanalyzed to diagnose performance problems, monitor user interactions,and to derive other insights.

A number of tools are available to analyze machine data, that is,machine-generated data. In order to reduce the size of the potentiallyvast amount of machine data that may be generated, many of these toolstypically pre-process the data based on anticipated data-analysis needs.For example, pre-specified data items may be extracted from the machinedata and stored in a database to facilitate efficient retrieval andanalysis of those data items at search time. However, the rest of themachine data typically is not saved and discarded during pre-processing.As storage capacity becomes progressively cheaper and more plentiful,there are fewer incentives to discard these portions of machine data andmany reasons to retain more of the data.

This plentiful storage capacity is presently making it feasible to storemassive quantities of minimally processed machine data for laterretrieval and analysis. In general, storing minimally processed machinedata and performing analysis operations at search time can providegreater flexibility because it enables an analyst to search all of themachine data, instead of searching only a pre-specified set of dataitems. This may enable an analyst to investigate different aspects ofthe machine data that previously were unavailable for analysis.

However, analyzing and searching massive quantities of machine datapresents a number of challenges. For example, a data center, servers, ornetwork appliances may generate many different types and formats ofmachine data (e.g., system logs, network packet data (e.g., wire data,etc.), sensor data, application program data, error logs, stack traces,system performance data, operating system data, virtualization data,etc.) from thousands of different components, which can collectively bevery time-consuming to analyze. In another example, mobile devices maygenerate large amounts of information relating to data accesses,application performance, operating system performance, networkperformance, etc. There can be millions of mobile devices that reportthese types of information.

These challenges can be addressed by using an event-based data intakeand query system, such as the SPLUNK® ENTERPRISE system developed bySplunk Inc. of San Francisco, Calif. The SPLUNK® ENTERPRISE system isthe leading platform for providing real-time operational intelligencethat enables organizations to collect, index, and searchmachine-generated data from various websites, applications, servers,networks, and mobile devices that power their businesses. The SPLUNK®ENTERPRISE system is particularly useful for analyzing data which iscommonly found in system log files, network data, and other data inputsources. Although many of the techniques described herein are explainedwith reference to a data intake and query system similar to the SPLUNK®ENTERPRISE system, these techniques are also applicable to other typesof data systems.

In the SPLUNK® ENTERPRISE system, machine-generated data are collectedand stored as “events”. An event comprises a portion of themachine-generated data and is associated with a specific point in time.For example, events may be derived from “time series data,” where thetime series data comprises a sequence of data points (e.g., performancemeasurements from a computer system, etc.) that are associated withsuccessive points in time. In general, each event can be associated witha timestamp that is derived from the raw data in the event, determinedthrough interpolation between temporally proximate events having knowntimestamps, or determined based on other configurable rules forassociating timestamps with events, etc.

In some instances, machine data can have a predefined format, where dataitems with specific data formats are stored at predefined locations inthe data. For example, the machine data may include data stored asfields in a database table. In other instances, machine data may nothave a predefined format, that is, the data is not at fixed, predefinedlocations, but the data does have repeatable patterns and is not random.This means that some machine data can comprise various data items ofdifferent data types and that may be stored at different locationswithin the data. For example, when the data source is an operatingsystem log, an event can include one or more lines from the operatingsystem log containing raw data that includes different types ofperformance and diagnostic information associated with a specific pointin time.

Examples of components which may generate machine data from which eventscan be derived include, but are not limited to, web servers, applicationservers, databases, firewalls, routers, operating systems, and softwareapplications that execute on computer systems, mobile devices, sensors,Internet of Things (IoT) devices, etc. The data generated by such datasources can include, for example and without limitation, server logfiles, activity log files, configuration files, messages, network packetdata, performance measurements, sensor measurements, etc.

The SPLUNK® ENTERPRISE system uses flexible schema to specify how toextract information from the event data. A flexible schema may bedeveloped and redefined as needed. Note that a flexible schema may beapplied to event data “on the fly,” when it is needed (e.g., at searchtime, index time, ingestion time, etc.). When the schema is not appliedto event data until search time it may be referred to as a “late-bindingschema.”

During operation, the SPLUNK® ENTERPRISE system starts with raw inputdata (e.g., one or more system logs, streams of network packet data,sensor data, application program data, error logs, stack traces, systemperformance data, etc.).. The system divides this raw data into blocks(e.g., buckets of data, each associated with a specific time frame,etc.), and parses the raw data to produce timestamped events. The systemstores the timestamped events in a data store. The system enables usersto run queries against the stored data to, for example, retrieve eventsthat meet criteria specified in a query, such as containing certainkeywords or having specific values in defined fields. As used hereinthroughout, data that is part of an event is referred to as “eventdata”. In this context, the term “field” refers to a location in theevent data containing one or more values for a specific data item. Aswill be described in more detail herein, the fields are defined byextraction rules (e.g., regular expressions) that derive one or morevalues from the portion of raw machine data in each event that has aparticular field specified by an extraction rule. The set of values soproduced are semantically-related (such as IP address), even though theraw machine data in each event may be in different formats (e.g.,semantically-related values may be in different positions in the eventsderived from different sources).

As noted above, the SPLUNK® ENTERPRISE system utilizes a late-bindingschema to event data while performing queries on events. One aspect of alate-binding schema is applying “extraction rules” to event data toextract values for specific fields during search time. Morespecifically, the extraction rules for a field can include one or moreinstructions that specify how to extract a value for the field from theevent data. An extraction rule can generally include any type ofinstruction for extracting values from data in events. In some cases, anextraction rule comprises a regular expression where a sequence ofcharacters form a search pattern, in which case the rule is referred toas a “regex rule.” The system applies the regex rule to the event datato extract values for associated fields in the event data by searchingthe event data for the sequence of characters defined in the regex rule.

In the SPLUNK® ENTERPRISE system, a field extractor may be configured toautomatically generate extraction rules for certain field values in theevents when the events are being created, indexed, or stored, orpossibly at a later time. Alternatively, a user may manually defineextraction rules for fields using a variety of techniques. In contrastto a conventional schema for a database system, a late-binding schema isnot defined at data ingestion time. Instead, the late-binding schema canbe developed on an ongoing basis until the time a query is actuallyexecuted. This means that extraction rules for the fields in a query maybe provided in the query itself, or may be located during execution ofthe query. Hence, as a user learns more about the data in the events,the user can continue to refine the late-binding schema by adding newfields, deleting fields, or modifying the field extraction rules for usethe next time the schema is used by the system. Because the SPLUNK®ENTERPRISE system maintains the underlying raw data and useslate-binding schema for searching the raw data, it enables a user tocontinue investigating and learn valuable insights about the raw data.

In some embodiments, a common field name may be used to reference two ormore fields containing equivalent data items, even though the fields maybe associated with different types of events that possibly havedifferent data formats and different extraction rules. By enabling acommon field name to be used to identify equivalent fields fromdifferent types of events generated by disparate data sources, thesystem facilitates use of a “common information model” (CIM) across thedisparate data sources (further discussed with respect to FIG. 5).

2.0. Operating Environment

FIG. 1 illustrates a networked computer system 100 in which anembodiment may be implemented. Those skilled in the art would understandthat FIG. 1 represents one example of a networked computer system andother embodiments may use different arrangements.

The networked computer system 100 comprises one or more computingdevices. These one or more computing devices comprise any combination ofhardware and software configured to implement the various logicalcomponents described herein. For example, the one or more computingdevices may include one or more memories that store instructions forimplementing the various components described herein, one or morehardware processors configured to execute the instructions stored in theone or more memories, and various data repositories in the one or morememories for storing data structures utilized and manipulated by thevarious components.

In an embodiment, one or more client devices 102 are coupled to one ormore host devices 106 and a data intake and query system 108 via one ormore networks 104. Networks 104 broadly represent one or more LANs,WANs, cellular networks (e.g., LTE, HSPA, 3G, and other cellulartechnologies), and/or networks using any of wired, wireless, terrestrialmicrowave, or satellite links, and may include the public Internet.

2.1. Host Devices

In the illustrated embodiment, a system 100 includes one or more hostdevices 106. Host devices 106 may broadly include any number ofcomputers, virtual machine instances, and/or data centers that areconfigured to host or execute one or more instances of host applications114. In general, a host device 106 may be involved, directly orindirectly, in processing requests received from client devices 102.Each host device 106 may comprise, for example, one or more of a networkdevice, a web server, an application server, a database server, etc. Acollection of host devices 106 may be configured to implement anetwork-based service. For example, a provider of a network-basedservice may configure one or more host devices 106 and host applications114 (e.g., one or more web servers, application servers, databaseservers, etc.) to collectively implement the network-based application.

In general, client devices 102 communicate with one or more hostapplications 114 to exchange information. The communication between aclient device 102 and a host application 114 may, for example, be basedon the Hypertext Transfer Protocol (HTTP) or any other network protocol.Content delivered from the host application 114 to a client device 102may include, for example, HTML documents, media content, etc. Thecommunication between a client device 102 and host application 114 mayinclude sending various requests and receiving data packets. Forexample, in general, a client device 102 or application running on aclient device may initiate communication with a host application 114 bymaking a request for a specific resource (e.g., based on an HTTPrequest), and the application server may respond with the requestedcontent stored in one or more response packets.

In the illustrated embodiment, one or more of host applications 114 maygenerate various types of performance data during operation, includingevent logs, network data, sensor data, and other types ofmachine-generated data. For example, a host application 114 comprising aweb server may generate one or more web server logs in which details ofinteractions between the web server and any number of client devices 102is recorded. As another example, a host device 106 comprising a routermay generate one or more router logs that record information related tonetwork traffic managed by the router. As yet another example, a hostapplication 114 comprising a database server may generate one or morelogs that record information related to requests sent from other hostapplications 114 (e.g., web servers or application servers) for datamanaged by the database server.

2.2. Client Devices

Client devices 102 of FIG. 1 represent any computing device capable ofinteracting with one or more host devices 106 via a network 104.Examples of client devices 102 may include, without limitation, smartphones, tablet computers, handheld computers, wearable devices, laptopcomputers, desktop computers, servers, portable media players, gamingdevices, and so forth. In general, a client device 102 can provideaccess to different content, for instance, content provided by one ormore host devices 106, etc. Each client device 102 may comprise one ormore client applications 110, described in more detail in a separatesection hereinafter.

2.3. Client Device Applications

In an embodiment, each client device 102 may host or execute one or moreclient applications 110 that are capable of interacting with one or morehost devices 106 via one or more networks 104. For instance, a clientapplication 110 may be or comprise a web browser that a user may use tonavigate to one or more websites or other resources provided by one ormore host devices 106. As another example, a client application 110 maycomprise a mobile application or “app.” For example, an operator of anetwork-based service hosted by one or more host devices 106 may makeavailable one or more mobile apps that enable users of client devices102 to access various resources of the network-based service. As yetanother example, client applications 110 may include backgroundprocesses that perform various operations without direct interactionfrom a user. A client application 110 may include a “plug-in” or“extension” to another application, such as a web browser plug-in orextension.

In an embodiment, a client application 110 may include a monitoringcomponent 112. At a high level, the monitoring component 112 comprises asoftware component or other logic that facilitates generatingperformance data related to a client device's operating state, includingmonitoring network traffic sent and received from the client device andcollecting other device and/or application-specific information.Monitoring component 112 may be an integrated component of a clientapplication 110, a plug-in, an extension, or any other type of add-oncomponent. Monitoring component 112 may also be a stand-alone process.

In one embodiment, a monitoring component 112 may be created when aclient application 110 is developed, for example, by an applicationdeveloper using a software development kit (SDK). The SDK may includecustom monitoring code that can be incorporated into the codeimplementing a client application 110. When the code is converted to anexecutable application, the custom code implementing the monitoringfunctionality can become part of the application itself.

In some cases, an SDK or other code for implementing the monitoringfunctionality may be offered by a provider of a data intake and querysystem, such as a system 108. In such cases, the provider of the system108 can implement the custom code so that performance data generated bythe monitoring functionality is sent to the system 108 to facilitateanalysis of the performance data by a developer of the clientapplication or other users.

In an embodiment, the custom monitoring code may be incorporated intothe code of a client application 110 in a number of different ways, suchas the insertion of one or more lines in the client application codethat call or otherwise invoke the monitoring component 112. As such, adeveloper of a client application 110 can add one or more lines of codeinto the client application 110 to trigger the monitoring component 112at desired points during execution of the application. Code thattriggers the monitoring component may be referred to as a monitortrigger. For instance, a monitor trigger may be included at or near thebeginning of the executable code of the client application 110 such thatthe monitoring component 112 is initiated or triggered as theapplication is launched, or included at other points in the code thatcorrespond to various actions of the client application, such as sendinga network request or displaying a particular interface.

In an embodiment, the monitoring component 112 may monitor one or moreaspects of network traffic sent and/or received by a client application110. For example, the monitoring component 112 may be configured tomonitor data packets transmitted to and/or from one or more hostapplications 114. Incoming and/or outgoing data packets can be read orexamined to identify network data contained within the packets, forexample, and other aspects of data packets can be analyzed to determinea number of network performance statistics. Monitoring network trafficmay enable information to be gathered particular to the networkperformance associated with a client application 110 or set ofapplications.

In an embodiment, network performance data refers to any type of datathat indicates information about the network and/or network performance.Network performance data may include, for instance, a URL requested, aconnection type (e.g., HTTP, HTTPS, etc.), a connection start time, aconnection end time, an HTTP status code, request length, responselength, request headers, response headers, connection status (e.g.,completion, response time(s), failure, etc.), and the like. Uponobtaining network performance data indicating performance of thenetwork, the network performance data can be transmitted to a dataintake and query system 108 for analysis.

Upon developing a client application 110 that incorporates a monitoringcomponent 112, the client application 110 can be distributed to clientdevices 102. Applications generally can be distributed to client devices102 in any manner, or they can be pre-loaded. In some cases, theapplication may be distributed to a client device 102 via an applicationmarketplace or other application distribution system. For instance, anapplication marketplace or other application distribution system mightdistribute the application to a client device based on a request fromthe client device to download the application.

Examples of functionality that enables monitoring performance of aclient device are described in U.S. patent application Ser. No.14/524,748, entitled “UTILIZING PACKET HEADERS TO MONITOR NETWORKTRAFFIC IN ASSOCIATION WITH A CLIENT DEVICE”, filed on 27 Oct. 2014, andwhich is hereby incorporated by reference in its entirety for allpurposes.

In an embodiment, the monitoring component 112 may also monitor andcollect performance data related to one or more aspects of theoperational state of a client application 110 and/or client device 102.For example, a monitoring component 112 may be configured to collectdevice performance information by monitoring one or more client deviceoperations, or by making calls to an operating system and/or one or moreother applications executing on a client device 102 for performanceinformation. Device performance information may include, for instance, acurrent wireless signal strength of the device, a current connectiontype and network carrier, current memory performance information, ageographic location of the device, a device orientation, and any otherinformation related to the operational state of the client device.

In an embodiment, the monitoring component 112 may also monitor andcollect other device profile information including, for example, a typeof client device, a manufacturer and model of the device, versions ofvarious software applications installed on the device, and so forth.

In general, a monitoring component 112 may be configured to generateperformance data in response to a monitor trigger in the code of aclient application 110 or other triggering application event, asdescribed above, and to store the performance data in one or more datarecords. Each data record, for example, may include a collection offield-value pairs, each field-value pair storing a particular item ofperformance data in association with a field for the item. For example,a data record generated by a monitoring component 112 may include a“networkLatency” field (not shown in the Figure) in which a value isstored. This field indicates a network latency measurement associatedwith one or more network requests. The data record may include a “state”field to store a value indicating a state of a network connection, andso forth for any number of aspects of collected performance data.

2.4. Data Server System

FIG. 2 depicts a block diagram of an example data intake and querysystem 108, similar to the SPLUNK® ENTERPRISE system. System 108includes one or more forwarders 204 that receive data from a variety ofinput data sources 202, and one or more indexers 206 that process andstore the data in one or more data stores 208. These forwarders andindexers can comprise separate computer systems, or may alternativelycomprise separate processes executing on one or more computer systems.

Each of the components of a system 108 (e.g., forwarders, indexers andsearch heads) may at times refer to various configuration files storedlocally at each component. These configuration files typically mayinvolve some level of user configuration to accommodate particular typesof data a user desires to analyze and to account for other userpreferences. Embodiments described herein provide a central repositoryfor maintaining configuration files for the components of a system 108.Various components of a system 108 (e.g., search heads) are able toreceive changes to the configuration files through a search headinterface or a central management interface. Other components of asystem 108 (e.g., indexers and forwarders) are able to receive changesto the configuration files from the central repository. Thus, thetechniques and systems described herein for providing user interfacesthat enable a user to configure source type definitions are applicableto both on-premises and cloud-based service contexts, or somecombination thereof (e.g., a hybrid system where both an on-premisesenvironment such as SPLUNK® ENTERPRISE and a cloud-based environmentsuch as SPLUNK CLOUD™ are centrally visible).

Each data source 202 broadly represents a distinct source of data thatcan be consumed by a system 108. Examples of a data source 202 include,without limitation, data files, directories of files, data sent over anetwork, event logs, registries, etc.

During operation, the forwarders 204 identify which indexers 206 receivedata collected from a data source 202 and forward the data to theappropriate indexers. Forwarders 204 can also perform operations on thedata before forwarding, including removing extraneous data, detectingtimestamps in the data, parsing data, indexing data, routing data basedon criteria relating to the data being routed, and/or performing otherdata transformations.

In an embodiment, a forwarder 204 may comprise a service accessible toclient devices 102 and host devices 106 via a network 104. For example,one type of forwarder 204 may be capable of consuming vast amounts ofreal-time data from a potentially large number of client devices 102and/or host devices 106. The forwarder 204 may, for example, comprise acomputing device which implements multiple data pipelines or “queues” tohandle forwarding of network data to indexers 206. A forwarder 204 mayalso perform many of the functions that are performed by an indexer. Forexample, a forwarder 204 may perform keyword extractions on raw data orparse raw data to create events. A forwarder 204 may generate timestamps for events. Additionally or alternatively, a forwarder 204 mayperform routing of events to indexers. Data store 208 may contain eventsderived from machine data from a variety of sources all pertaining tothe same component in an IT environment, and this data may be producedby the machine in question or by other components in the IT environment.

2.5. Data Ingestion

FIG. 3 depicts a flow chart illustrating an example data flow performedby Data Intake and Query system 108, in accordance with the disclosedembodiments. The data flow illustrated in FIG. 3 is provided forillustrative purposes only; those skilled in the art would understandthat one or more of the steps of the processes illustrated in FIG. 3 maybe removed or the ordering of the steps may be changed. Furthermore, forthe purposes of illustrating a clear example, one or more particularsystem components are described in the context of performing variousoperations during each of the data flow stages. For example, a forwarderis described as receiving and processing data during an input phase; anindexer is described as parsing and indexing data during parsing andindexing phases; and a search head is described as performing a searchquery during a search phase. However, other system arrangements anddistributions of the processing steps across system components may beused.

2.5.1. Input

At block 302, a forwarder receives data from an input source, such as adata source 202 shown in FIG. 2. A forwarder initially may receive thedata as a raw data stream generated by the input source. For example, aforwarder may receive a data stream from a log file generated by anapplication server, from a stream of network data from a network device,or from any other source of data. In one embodiment, a forwarderreceives the raw data and may segment the data stream into “blocks”, or“buckets,” possibly of a uniform data size, to facilitate subsequentprocessing steps.

At block 304, a forwarder or other system component annotates each blockgenerated from the raw data with one or more metadata fields. Thesemetadata fields may, for example, provide information related to thedata block as a whole and may apply to each event that is subsequentlyderived from the data in the data block. For example, the metadatafields may include separate fields specifying each of a host, a source,and a source type related to the data block. A host field may contain avalue identifying a host name or IP address of a device that generatedthe data. A source field may contain a value identifying a source of thedata, such as a pathname of a file or a protocol and port related toreceived network data. A source type field may contain a valuespecifying a particular source type label for the data. Additionalmetadata fields may also be included during the input phase, such as acharacter encoding of the data, if known, and possibly other values thatprovide information relevant to later processing steps. In anembodiment, a forwarder forwards the annotated data blocks to anothersystem component (typically an indexer) for further processing.

The SPLUNK® ENTERPRISE system allows forwarding of data from one SPLUNK®ENTERPRISE instance to another, or even to a third-party system. SPLUNK®ENTERPRISE system can employ different types of forwarders in aconfiguration.

In an embodiment, a forwarder may contain the essential componentsneeded to forward data. It can gather data from a variety of inputs andforward the data to a SPLUNK® ENTERPRISE server for indexing andsearching. It also can tag metadata (e.g., source, source type, host,etc.).

Additionally or optionally, in an embodiment, a forwarder has thecapabilities of the aforementioned forwarder as well as additionalcapabilities. The forwarder can parse data before forwarding the data(e.g., associate a time stamp with a portion of data and create anevent, etc.) and can route data based on criteria such as source or typeof event. It can also index data locally while forwarding the data toanother indexer.

2.5.2. Parsing

At block 306, an indexer receives data blocks from a forwarder andparses the data to organize the data into events. In an embodiment, toorganize the data into events, an indexer may determine a source typeassociated with each data block (e.g., by extracting a source type labelfrom the metadata fields associated with the data block, etc.) and referto a source type configuration corresponding to the identified sourcetype. The source type definition may include one or more properties thatindicate to the indexer to automatically determine the boundaries ofevents within the data. In general, these properties may include regularexpression-based rules or delimiter rules where, for example, eventboundaries may be indicated by predefined characters or characterstrings. These predefined characters may include punctuation marks orother special characters including, for example, carriage returns, tabs,spaces, line breaks, etc. If a source type for the data is unknown tothe indexer, an indexer may infer a source type for the data byexamining the structure of the data. Then, it can apply an inferredsource type definition to the data to create the events.

At block 308, the indexer determines a timestamp for each event. Similarto the process for creating events, an indexer may again refer to asource type definition associated with the data to locate one or moreproperties that indicate instructions for determining a timestamp foreach event. The properties may, for example, instruct an indexer toextract a time value from a portion of data in the event, to interpolatetime values based on timestamps associated with temporally proximateevents, to create a timestamp based on a time the event data wasreceived or generated, to use the timestamp of a previous event, or useany other rules for determining timestamps.

At block 310, the indexer associates with each event one or moremetadata fields including a field containing the timestamp (in someembodiments, a timestamp may be included in the metadata fields)determined for the event. These metadata fields may include a number of“default fields” that are associated with all events, and may alsoinclude one more custom fields as defined by a user. Similar to themetadata fields associated with the data blocks at block 304, thedefault metadata fields associated with each event may include a host,source, and source type field including or in addition to a fieldstoring the timestamp.

At block 312, an indexer may optionally apply one or moretransformations to data included in the events created at block 306. Forexample, such transformations can include removing a portion of an event(e.g., a portion used to define event boundaries, extraneous charactersfrom the event, other extraneous text, etc.), masking a portion of anevent (e.g., masking a credit card number), removing redundant portionsof an event, etc. The transformations applied to event data may, forexample, be specified in one or more configuration files and referencedby one or more source type definitions.

2.5.3. Indexing

At blocks 314 and 316, an indexer can optionally generate a keywordindex to facilitate fast keyword searching for event data. To build akeyword index, at block 314, the indexer identifies a set of keywords ineach event. At block 316, the indexer includes the identified keywordsin an index, which associates each stored keyword with referencepointers to events containing that keyword (or to locations withinevents where that keyword is located, other location identifiers, etc.).When an indexer subsequently receives a keyword-based query, the indexercan access the keyword index to quickly identify events containing thekeyword.

In some embodiments, the keyword index may include entries forname-value pairs found in events, where a name-value pair can include apair of keywords connected by a symbol, such as an equals sign or colon.This way, events containing these name-value pairs can be quicklylocated. In some embodiments, fields can automatically be generated forsome or all of the name-value pairs at the time of indexing. Forexample, if the string “dest=10.0.1.2” is found in an event, a fieldnamed “dent” may be created for the event, and assigned a value of“10.0.1.2”.

At block 318, the indexer stores the events with an associated timestampin a data store 208. Timestamps enable a user to search for events basedon a time range. In one embodiment, the stored events are organized into“buckets,” where each bucket stores events associated with a specifictime range based on the timestamps associated with each event. This maynot only improve time-based searching, but also allows for events withrecent timestamps, which may have a higher likelihood of being accessed,to be stored in a faster memory to facilitate faster retrieval. Forexample, buckets containing the most recent events can be stored inflash memory rather than on a hard disk.

Each indexer 206 may be responsible for storing and searching a subsetof the events contained in a corresponding data store 208. Bydistributing events among the indexers and data stores, the indexers cananalyze events for a query in parallel. For example, using map-reducetechniques, each indexer returns partial responses for a subset ofevents to a search head that combines the results to produce an answerfor the query. By storing events in buckets for specific time ranges, anindexer may further optimize data retrieval process by searching bucketscorresponding to time ranges that are relevant to a query.

Moreover, events and buckets can also be replicated across differentindexers and data stores to facilitate high availability and disasterrecovery as described in U.S. patent application Ser. No. 14/266,812,entitled “SITE-BASED SEARCH AFFINITY”, filed on 30 Apr. 2014, and inU.S. patent application Ser. No. 14/266,817, entitled “MULTI-SITECLUSTERING”, also filed on 30 Apr. 2014, each of which is herebyincorporated by reference in its entirety for all purposes.

2.6. Query Processing

FIG. 4 is a flow diagram that illustrates an example process that asearch head and one or more indexers may perform during a search query.At block 402, a search head receives a search query from a client. Atblock 404, the search head analyzes the search query to determine whatportion(s) of the query can be delegated to indexers and what portionsof the query can be executed locally by the search head. At block 406,the search head distributes the determined portions of the query to theappropriate indexers. In an embodiment, a search head cluster may takethe place of an independent search head where each search head in thesearch head cluster coordinates with peer search heads in the searchhead cluster to schedule jobs, replicate search results, updateconfigurations, fulfill search requests, etc. In an embodiment, thesearch head (or each search head) communicates with a master node (alsoknown as a cluster master, not shown in Fig.) that provides the searchhead with a list of indexers to which the search head can distribute thedetermined portions of the query. The master node maintains a list ofactive indexers and can also designate which indexers may haveresponsibility for responding to queries over certain sets of events. Asearch head may communicate with the master node before the search headdistributes queries to indexers to discover the addresses of activeindexers.

At block 408, the indexers to which the query was distributed, searchdata stores associated with them for events that are responsive to thequery. To determine which events are responsive to the query, theindexer searches for events that match the criteria specified in thequery. These criteria can include matching keywords or specific valuesfor certain fields. The searching operations at block 408 may use thelate-binding schema to extract values for specified fields from eventsat the time the query is processed. In an embodiment, one or more rulesfor extracting field values may be specified as part of a source typedefinition. The indexers may then either send the relevant events backto the search head, or use the events to determine a partial result, andsend the partial result back to the search head.

At block 410, the search head combines the partial results and/or eventsreceived from the indexers to produce a final result for the query. Thisfinal result may comprise different types of data depending on what thequery requested. For example, the results can include a listing ofmatching events returned by the query, or some type of visualization ofthe data from the returned events. In another example, the final resultcan include one or more calculated values derived from the matchingevents.

The results generated by the system 108 can be returned to a clientusing different techniques. For example, one technique streams resultsor relevant events back to a client in real-time as they are identified.Another technique waits to report the results to the client until acomplete set of results (which may include a set of relevant events or aresult based on relevant events) is ready to return to the client. Yetanother technique streams interim results or relevant events back to theclient in real-time until a complete set of results is ready, and thenreturns the complete set of results to the client. In another technique,certain results are stored as “search jobs” and the client may retrievethe results by referring the search jobs.

The search head can also perform various operations to make the searchmore efficient. For example, before the search head begins execution ofa query, the search head can determine a time range for the query and aset of common keywords that all matching events include. The search headmay then use these parameters to query the indexers to obtain a supersetof the eventual results. Then, during a filtering stage, the search headcan perform field-extraction operations on the superset to produce areduced set of search results. This speeds up queries that are performedon a periodic basis.

2.7. Field Extraction

The search head 210 allows users to search and visualize event dataextracted from raw machine data received from homogenous data sources.It also allows users to search and visualize event data extracted fromraw machine data received from heterogeneous data sources. The searchhead 210 includes various mechanisms, which may additionally reside inan indexer 206, for processing a query. Splunk Processing Language(SPL), used in conjunction with the SPLUNK® ENTERPRISE system, can beutilized to make a query. SPL is a pipelined search language in which aset of inputs is operated on by a first command in a command line, andthen a subsequent command following the pipe symbol “1” operates on theresults produced by the first command, and so on for additionalcommands. Other query languages, such as the Structured Query Language(“SQL”), can be used to create a query.

In response to receiving the search query, search head 210 usesextraction rules to extract values for the fields associated with afield or fields in the event data being searched. The search head 210obtains extraction rules that specify how to extract a value for certainfields from an event. Extraction rules can comprise regex rules thatspecify how to extract values for the relevant fields. In addition tospecifying how to extract field values, the extraction rules may alsoinclude instructions for deriving a field value by performing a functionon a character string or value retrieved by the extraction rule. Forexample, a transformation rule may truncate a character string, orconvert the character string into a different data format. In somecases, the query itself can specify one or more extraction rules.

The search head 210 can apply the extraction rules to event data that itreceives from indexers 206. Indexers 206 may apply the extraction rulesto events in an associated data store 208. Extraction rules can beapplied to all the events in a data store, or to a subset of the eventsthat have been filtered based on some criteria (e.g., event time stampvalues, etc.). Extraction rules can be used to extract one or morevalues for a field from events by parsing the event data and examiningthe event data for one or more patterns of characters, numbers,delimiters, etc., that indicate where the field begins and, optionally,ends.

FIG. 5 illustrates an example of raw machine data received fromdisparate data sources. In this example, a user submits an order formerchandise using a vendor's shopping application program 501 running onthe user's system. In this example, the order was not delivered to thevendor's server due to a resource exception at the destination serverthat is detected by the middleware code 502. The user then sends amessage to the customer support 503 to complain about the order failingto complete. The three systems 501, 502, and 503 are disparate systemsthat do not have a common logging format. The order application 501sends log data 504 to the SPLUNK® ENTERPRISE system in one format, themiddleware code 502 sends error log data 505 in a second format, and thesupport server 503 sends log data 506 in a third format.

Using the log data received at one or more indexers 206 from the threesystems the vendor can uniquely obtain an insight into user activity,user experience, and system behavior. The search head 210 allows thevendor's administrator to search the log data from the three systemsthat one or more indexers 206 are responsible for searching, therebyobtaining correlated information, such as the order number andcorresponding customer ID number of the person placing the order. Thesystem also allows the administrator to see a visualization of relatedevents via a user interface. The administrator can query the search head210 for customer ID field value matches across the log data from thethree systems that are stored at the one or more indexers 206. Thecustomer ID field value exists in the data gathered from the threesystems, but the customer ID field value may be located in differentareas of the data given differences in the architecture of thesystems—there is a semantic relationship between the customer ID fieldvalues generated by the three systems. The search head 210 requestsevent data from the one or more indexers 206 to gather relevant eventdata from the three systems. It then applies extraction rules to theevent data in order to extract field values that it can correlate. Thesearch head may apply a different extraction rule to each set of eventsfrom each system when the event data format differs among systems. Inthis example, the user interface can display to the administrator theevent data corresponding to the common customer ID field values 507,508, and 509, thereby providing the administrator with insight into acustomer's experience.

Note that query results can be returned to a client, a search head, orany other system component for further processing. In general, queryresults may include a set of one or more events, a set of one or morevalues obtained from the events, a subset of the values, statisticscalculated based on the values, a report containing the values, or avisualization, such as a graph or chart, generated from the values.

2.8. Example Search Screen

FIG. 6A illustrates an example search screen 600 in accordance with thedisclosed embodiments. Search screen 600 includes a search bar 602 thataccepts user input in the form of a search string. It also includes atime range picker 612 that enables the user to specify a time range forthe search. For “historical searches” the user can select a specifictime range, or alternatively a relative time range, such as “today,”“yesterday” or “last week.” For “real-time searches,” the user canselect the size of a preceding time window to search for real-timeevents. Search screen 600 also initially displays a “data summary”dialog as is illustrated in FIG. 6B that enables the user to selectdifferent sources for the event data, such as by selecting specifichosts and log files.

After the search is executed, the search screen 600 in FIG. 6A candisplay the results through search results tabs 604, wherein searchresults tabs 604 includes: an “events tab” that displays variousinformation about events returned by the search; a “statistics tab” thatdisplays statistics about the search results; and a “visualization tab”that displays various visualizations of the search results. The eventstab illustrated in FIG. 6A displays a timeline graph 605 thatgraphically illustrates the number of events that occurred in one-hourintervals over the selected time range. It also displays an events list608 that enables a user to view the raw data in each of the returnedevents. It additionally displays a fields sidebar 606 that includesstatistics about occurrences of specific fields in the returned events,including “selected fields” that are pre-selected by the user, and“interesting fields” that are automatically selected by the system basedon pre-specified criteria.

2.9. Data Models

A data model is a hierarchically structured search-time mapping ofsemantic knowledge about one or more datasets. It encodes the domainknowledge necessary to build a variety of specialized searches of thosedatasets. Those searches, in turn, can be used to generate reports.

A data model is composed of one or more “objects” (or “data modelobjects”) that define or otherwise correspond to a specific set of data.

Objects in data models can be arranged hierarchically in parent/childrelationships. Each child object represents a subset of the datasetcovered by its parent object. The top-level objects in data models arecollectively referred to as “root objects.”

Child objects have inheritance. Data model objects are defined bycharacteristics that mostly break down into constraints and attributes.Child objects inherit constraints and attributes from their parentobjects and have additional constraints and attributes of their own.Child objects provide a way of filtering events from parent objects.Because a child object always provides an additional constraint inaddition to the constraints it has inherited from its parent object, thedataset it represents is always a subset of the dataset that its parentrepresents.

For example, a first data model object may define a broad set of datapertaining to e-mail activity generally, and another data model objectmay define specific datasets within the broad dataset, such as a subsetof the e-mail data pertaining specifically to e-mails sent. Examples ofdata models can include electronic mail, authentication, databases,intrusion detection, malware, application state, alerts, computeinventory, network sessions, network traffic, performance, audits,updates, vulnerabilities, etc. Data models and their objects can bedesigned by knowledge managers in an organization, and they can enabledownstream users to quickly focus on a specific set of data. Forexample, a user can simply select an “e-mail activity” data model objectto access a dataset relating to e-mails generally (e.g., sent orreceived), or select an “e-mails sent” data model object (or datasub-model object) to access a dataset relating to e-mails sent.

A data model object may be defined by (1) a set of search constraints,and (2) a set of fields. Thus, a data model object can be used toquickly search data to identify a set of events and to identify a set offields to be associated with the set of events. For example, an “e-mailssent” data model object may specify a search for events relating toe-mails that have been sent, and specify a set of fields that areassociated with the events. Thus, a user can retrieve and use the“e-mails sent” data model object to quickly search source data forevents relating to sent e-mails, and may be provided with a listing ofthe set of fields relevant to the events in a user interface screen.

A child of the parent data model may be defined by a search (typically anarrower search) that produces a subset of the events that would beproduced by the parent data model's search. The child's set of fieldscan include a subset of the set of fields of the parent data modeland/or additional fields. Data model objects that reference the subsetscan be arranged in a hierarchical manner, so that child subsets ofevents are proper subsets of their parents. A user iteratively applies amodel development tool (not shown in Fig.) to prepare a query thatdefines a subset of events and assigns an object name to that subset. Achild subset is created by further limiting a query that generated aparent subset. A late-binding schema of field extraction rules isassociated with each object or subset in the data model.

Data definitions in associated schemas can be taken from the commoninformation model (CIM) or can be devised for a particular schema andoptionally added to the CIM. Child objects inherit fields from parentsand can include fields not present in parents. A model developer canselect fewer extraction rules than are available for the sourcesreturned by the query that defines events belonging to a model.Selecting a limited set of extraction rules can be a tool forsimplifying and focusing the data model, while allowing a userflexibility to explore the data subset. Development of a data model isfurther explained in U.S. Pat. Nos. 8,788,525 and 8,788,526, bothentitled “DATA MODEL FOR MACHINE DATA FOR SEMANTIC SEARCH”, both issuedon 22 Jul. 2014, U.S. Pat. No. 8,983,994, entitled “GENERATION OF A DATAMODEL FOR SEARCHING MACHINE DATA”, issued on 17 Mar., 2015, U.S. patentapplication Ser. No. 14/611,232, entitled “GENERATION OF A DATA MODELAPPLIED TO QUERIES”, filed on 31 Jan. 2015, and U.S. patent applicationSer. No. 14/815,884, entitled “GENERATION OF A DATA MODEL APPLIED TOOBJECT QUERIES”, filed on 31 Jul. 2015, each of which is herebyincorporated by reference in its entirety for all purposes. See, also,Knowledge Manager Manual, Build a Data Model, Splunk Enterprise 6.1.3pp. 150-204 (Aug. 25, 2014).

A data model can also include reports. One or more report formats can beassociated with a particular data model and be made available to runagainst the data model. A user can use child objects to design reportswith object datasets that already have extraneous data pre-filtered out.In an embodiment, the data intake and query system 108 provides the userwith the ability to produce reports (e.g., a table, chart,visualization, etc.) without having to enter SPL, SQL, or other querylanguage terms into a search screen. Data models are used as the basisfor the search feature.

Data models may be selected in a report generation interface. The reportgenerator supports drag-and-drop organization of fields to be summarizedin a report. When a model is selected, the fields with availableextraction rules are made available for use in the report. The user mayrefine and/or filter search results to produce more precise reports. Theuser may select some fields for organizing the report and select otherfields for providing detail according to the report organization. Forexample, “region” and “salesperson” are fields used for organizing thereport and sales data can be summarized (subtotaled and totaled) withinthis organization. The report generator allows the user to specify oneor more fields within events and apply statistical analysis on valuesextracted from the specified one or more fields. The report generatormay aggregate search results across sets of events and generatestatistics based on aggregated search results. Building reports usingthe report generation interface is further explained in U.S. patentapplication Ser. No. 14/503,335, entitled “GENERATING REPORTS FROMUNSTRUCTURED DATA”, filed on 30 Sep. 2014, and which is herebyincorporated by reference in its entirety for all purposes, and in PivotManual, Splunk Enterprise 6.1.3 (Aug. 4, 2014). Data visualizations alsocan be generated in a variety of formats, by reference to the datamodel. Reports, data visualizations, and data model objects can be savedand associated with the data model for future use. The data model objectmay be used to perform searches of other data.

FIGS. 12, 13, and 7A-7D illustrate a series of user interface screenswhere a user may select report generation options using data models. Thereport generation process may be driven by a predefined data modelobject, such as a data model object defined and/or saved via a reportingapplication or a data model object obtained from another source. A usercan load a saved data model object using a report editor. For example,the initial search query and fields used to drive the report editor maybe obtained from a data model object. The data model object that is usedto drive a report generation process may define a search and a set offields. Upon loading of the data model object, the report generationprocess may enable a user to use the fields (e.g., the fields defined bythe data model object) to define criteria for a report (e.g., filters,split rows/columns, aggregates, etc.) and the search may be used toidentify events (e.g., to identify events responsive to the search) usedto generate the report. That is, for example, if a data model object isselected to drive a report editor, the graphical user interface of thereport editor may enable a user to define reporting criteria for thereport using the fields associated with the selected data model object,and the events used to generate the report may be constrained to theevents that match, or otherwise satisfy, the search constraints of theselected data model object.

The selection of a data model object for use in driving a reportgeneration may be facilitated by a data model object selectioninterface. FIG. 12 illustrates an example interactive data modelselection graphical user interface 1200 of a report editor that displaysa listing of available data models 1201. The user may select one of thedata models 1202.

FIG. 13 illustrates an example data model object selection graphicaluser interface 1300 that displays available data objects 1301 for theselected data object model 1202. The user may select one of thedisplayed data model objects 1302 for use in driving the reportgeneration process.

Once a data model object is selected by the user, a user interfacescreen 700 shown in FIG. 7A may display an interactive listing ofautomatic field identification options 701 based on the selected datamodel object. For example, a user may select one of the threeillustrated options (e.g., the “All Fields” option 702, the “SelectedFields” option 703, or the “Coverage” option (e.g., fields with at leasta specified % of coverage) 704). If the user selects the “All Fields”option 702, all of the fields identified from the events that werereturned in response to an initial search query may be selected. Thatis, for example, all of the fields of the identified data model objectfields may be selected. If the user selects the “Selected Fields” option703, only the fields from the fields of the identified data model objectfields that are selected by the user may be used. If the user selectsthe “Coverage” option 704, only the fields of the identified data modelobject fields meeting a specified coverage criteria may be selected. Apercent coverage may refer to the percentage of events returned by theinitial search query that a given field appears in. Thus, for example,if an object dataset includes 10,000 events returned in response to aninitial search query, and the “avg_age” field appears in 854 of those10,000 events, then the “avg_age” field would have a coverage of 8.54%for that object dataset. If, for example, the user selects the“Coverage” option and specifies a coverage value of 2%, only fieldshaving a coverage value equal to or greater than 2% may be selected. Thenumber of fields corresponding to each selectable option may bedisplayed in association with each option. For example, “97” displayednext to the “All Fields” option 702 indicates that 97 fields will beselected if the “All Fields” option is selected. The “3” displayed nextto the “Selected Fields” option 703 indicates that 3 of the 97 fieldswill be selected if the “Selected Fields” option is selected. The “49”displayed next to the “Coverage” option 704 indicates that 49 of the 97fields (e.g., the 49 fields having a coverage of 2% or greater) will beselected if the “Coverage” option is selected. The number of fieldscorresponding to the “Coverage” option may be dynamically updated basedon the specified percent of coverage.

FIG. 7B illustrates an example graphical user interface screen (alsocalled the pivot interface) 705 displaying the reporting application's“Report Editor” page. The screen may display interactive elements fordefining various elements of a report. For example, the page includes a“Filters” element 706, a “Split Rows” element 707, a “Split Columns”element 708, and a “Column Values” element 709. The page may include alist of search results 711. In this example, the Split Rows element 707is expanded, revealing a listing of fields 710 that can be used todefine additional criteria (e.g., reporting criteria). The listing offields 710 may correspond to the selected fields (attributes). That is,the listing of fields 710 may list only the fields previously selected,either automatically and/or manually by a user. FIG. 7C illustrates aformatting dialogue 712 that may be displayed upon selecting a fieldfrom the listing of fields 710. The dialogue can be used to format thedisplay of the results of the selection (e.g., label the column to bedisplayed as “component”).

FIG. 7D illustrates an example graphical user interface screen 705including a table of results 713 based on the selected criteriaincluding splitting the rows by the “component” field. A column 714having an associated count for each component listed in the table may bedisplayed that indicates an aggregate count of the number of times thatthe particular field-value pair (e.g., the value in a row) occurs in theset of events responsive to the initial search query.

FIG. 14 illustrates an example graphical user interface screen 1400 thatallows the user to filter search results and to perform statisticalanalysis on values extracted from specific fields in the set of events.In this example, the top ten product names ranked by price are selectedas a filter 1401 that causes the display of the ten most popularproducts sorted by price. Each row is displayed by product name andprice 1402. This results in each product displayed in a column labeled“product name” along with an associated price in a column labeled“price” 1406. Statistical analysis of other fields in the eventsassociated with the ten most popular products have been specified ascolumn values 1403. A count of the number of successful purchases foreach product is displayed in column 1404. This statistics may beproduced by filtering the search results by the product name, findingall occurrences of a successful purchase in a field within the eventsand generating a total of the number of occurrences. A sum of the totalsales is displayed in column 1405, which is a result of themultiplication of the price and the number of successful purchases foreach product.

The reporting application allows the user to create graphicalvisualizations of the statistics generated for a report. For example,FIG. 15 illustrates an example graphical user interface 1500 thatdisplays a set of components and associated statistics 1501. Thereporting application allows the user to select a visualization of thestatistics in a graph (e.g., bar chart, scatter plot, area chart, linechart, pie chart, radial gauge, marker gauge, filler gauge, etc.). FIG.16 illustrates an example of a bar chart visualization 1600 of an aspectof the statistical data 1501. FIG. 17 illustrates a scatter plotvisualization 1700 of an aspect of the statistical data 1501.

2.10. Acceleration Technique

The above-described system provides significant flexibility by enablinga user to analyze massive quantities of minimally processed data “on thefly” at search time instead of storing pre-specified portions of thedata in a database at ingestion time. This flexibility enables a user tosee valuable insights, correlate data, and perform subsequent queries toexamine interesting aspects of the data that may not have been apparentat ingestion time.

However, performing extraction and analysis operations at search timecan involve a large amount of data and require a large number ofcomputational operations, which can cause delays in processing thequeries. Advantageously, SPLUNK® ENTERPRISE system employs a number ofunique acceleration techniques that have been developed to speed upanalysis operations performed at search time. These techniques include:(1) performing search operations in parallel across multiple indexers;(2) using a keyword index; (3) using a high performance analytics store;and (4) accelerating the process of generating reports. These noveltechniques are described in more detail below.

2.10.1. Aggregation Technique

To facilitate faster query processing, a query can be structured suchthat multiple indexers perform the query in parallel, while aggregationof search results from the multiple indexers is performed locally at thesearch head. For example, FIG. 8 illustrates how a search query 802received from a client at a search head 210 can split into two phases,including: (1) subtasks 804 (e.g., data retrieval or simple filtering)that may be performed in parallel by indexers 206 for execution, and (2)a search results aggregation operation 806 to be executed by the searchhead when the results are ultimately collected from the indexers.

During operation, upon receiving search query 802, a search head 210determines that a portion of the operations involved with the searchquery may be performed locally by the search head. The search headmodifies search query 802 by substituting “stats” (create aggregatestatistics over results sets received from the indexers at the searchhead) with “prestats” (create statistics by the indexer from localresults set) to produce search query 804, and then distributes searchquery 804 to distributed indexers, which are also referred to as “searchpeers.” Note that search queries may generally specify search criteriaor operations to be performed on events that meet the search criteria.Search queries may also specify field names, as well as search criteriafor the values in the fields or operations to be performed on the valuesin the fields. Moreover, the search head may distribute the full searchquery to the search peers as illustrated in FIG. 4, or may alternativelydistribute a modified version (e.g., a more restricted version) of thesearch query to the search peers. In this example, the indexers areresponsible for producing the results and sending them to the searchhead. After the indexers return the results to the search head, thesearch head aggregates the received results 806 to form a single searchresult set. By executing the query in this manner, the systemeffectively distributes the computational operations across the indexerswhile minimizing data transfers.

2.10.2. Keyword Index

As described above with reference to the flow charts in FIG. 3 and FIG.4, data intake and query system 108 can construct and maintain one ormore keyword indices to quickly identify events containing specifickeywords. This technique can greatly speed up the processing of queriesinvolving specific keywords. As mentioned above, to build a keywordindex, an indexer first identifies a set of keywords. Then, the indexerincludes the identified keywords in an index, which associates eachstored keyword with references to events containing that keyword, or tolocations within events where that keyword is located. When an indexersubsequently receives a keyword-based query, the indexer can access thekeyword index to quickly identify events containing the keyword.

2.10.3. High Performance Analytics Store

To speed up certain types of queries, some embodiments of system 108create a high performance analytics store, which is referred to as a“summarization table,” that contains entries for specific field-valuepairs. Each of these entries keeps track of instances of a specificvalue in a specific field in the event data and includes references toevents containing the specific value in the specific field. For example,an example entry in a summarization table can keep track of occurrencesof the value “94107” in a “ZIP code” field of a set of events and theentry includes references to all of the events that contain the value“94107” in the ZIP code field. This optimization technique enables thesystem to quickly process queries that seek to determine how many eventshave a particular value for a particular field. To this end, the systemcan examine the entry in the summarization table to count instances ofthe specific value in the field without having to go through theindividual events or perform data extractions at search time. Also, ifthe system needs to process all events that have a specific field-valuecombination, the system can use the references in the summarizationtable entry to directly access the events to extract further informationwithout having to search all of the events to find the specificfield-value combination at search time.

In some embodiments, the system maintains a separate summarization tablefor each of the above-described time-specific buckets that stores eventsfor a specific time range. A bucket-specific summarization tableincludes entries for specific field-value combinations that occur inevents in the specific bucket. Alternatively, the system can maintain aseparate summarization table for each indexer. The indexer-specificsummarization table includes entries for the events in a data store thatare managed by the specific indexer. Indexer-specific summarizationtables may also be bucket-specific.

The summarization table can be populated by running a periodic querythat scans a set of events to find instances of a specific field-valuecombination, or alternatively instances of all field-value combinationsfor a specific field. A periodic query can be initiated by a user, orcan be scheduled to occur automatically at specific time intervals. Aperiodic query can also be automatically launched in response to a querythat asks for a specific field-value combination.

In some cases, when the summarization tables may not cover all of theevents that are relevant to a query, the system can use thesummarization tables to obtain partial results for the events that arecovered by summarization tables, but may also have to search throughother events that are not covered by the summarization tables to produceadditional results. These additional results can then be combined withthe partial results to produce a final set of results for the query. Thesummarization table and associated techniques are described in moredetail in U.S. Pat. No. 8,682,925, entitled “DISTRIBUTED HIGHPERFORMANCE ANALYTICS STORE”, issued on 25 Mar. 2014, U.S. patentapplication Ser. No. 14/170,159, entitled “SUPPLEMENTING A HIGHPERFORMANCE ANALYTICS STORE WITH EVALUATION OF INDIVIDUAL EVENTS TORESPOND TO AN EVENT QUERY”, filed on 31 Jan. 2014, and U.S. patentapplication Ser. No. 14/815,973, entitled “STORAGE MEDIUM AND CONTROLDEVICE”, filed on 21 Feb. 2014, each of which is hereby incorporated byreference in its entirety.

2.10.4. Accelerating Report Generation

In some embodiments, a data server system such as the SPLUNK® ENTERPRISEsystem can accelerate the process of periodically generating updatedreports based on query results. To accelerate this process, asummarization engine automatically examines the query to determinewhether generation of updated reports can be accelerated by creatingintermediate summaries. If reports can be accelerated, the summarizationengine periodically generates a summary covering data obtained during alatest non-overlapping time period. For example, where the query seeksevents meeting a specified criteria, a summary for the time periodincludes only events within the time period that meet the specifiedcriteria. Similarly, if the query seeks statistics calculated from theevents, such as the number of events that match the specified criteria,then the summary for the time period includes the number of events inthe period that match the specified criteria.

In addition to the creation of the summaries, the summarization engineschedules the periodic updating of the report associated with the query.During each scheduled report update, the query engine determines whetherintermediate summaries have been generated covering portions of the timeperiod covered by the report update. If so, then the report is generatedbased on the information contained in the summaries. Also, if additionalevent data has been received and has not yet been summarized, and isrequired to generate the complete report, the query can be run on thisadditional event data. Then, the results returned by this query on theadditional event data, along with the partial results obtained from theintermediate summaries, can be combined to generate the updated report.This process is repeated each time the report is updated. Alternatively,if the system stores events in buckets covering specific time ranges,then the summaries can be generated on a bucket-by-bucket basis. Notethat producing intermediate summaries can save the work involved inre-running the query for previous time periods, so advantageously onlythe newer event data needs to be processed while generating an updatedreport. These report acceleration techniques are described in moredetail in U.S. Pat. No. 8,589,403, entitled “COMPRESSED JOURNALING INEVENT TRACKING FILES FOR METADATA RECOVERY AND REPLICATION”, issued on19 Nov. 2013, U.S. Pat. No. 8,412,696, entitled “REAL TIME SEARCHING ANDREPORTING”, issued on 2 Apr. 2011, and U.S. Pat. Nos. 8,589,375 and8,589,432, both also entitled “REAL TIME SEARCHING AND REPORTING”, bothissued on 19 Nov. 2013, each of which is hereby incorporated byreference in its entirety.

2.11. Data Center Monitoring

As mentioned above, the SPLUNK® ENTERPRISE platform provides variousfeatures that simplify the developers' task to create variousapplications. One such application is SPLUNK® APP FOR VMWARE® thatprovides operational visibility into granular performance metrics, logs,tasks and events, and topology from hosts, virtual machines and virtualcenters. It empowers administrators with an accurate real-time pictureof the health of the environment, proactively identifying performanceand capacity bottlenecks.

Conventional data-center-monitoring systems lack the infrastructure toeffectively store and analyze large volumes of machine-generated data,such as performance information and log data obtained from the datacenter. In conventional data-center-monitoring systems,machine-generated data is typically pre-processed prior to being stored,for example, by extracting pre-specified data items and storing them ina database to facilitate subsequent retrieval and analysis at searchtime. However, the rest of the data is not saved and discarded duringpre-processing.

In contrast, the SPLUNK® APP FOR VMWARE® stores large volumes ofminimally processed machine data, such as performance information andlog data, at ingestion time for later retrieval and analysis at searchtime when a live performance issue is being investigated. In addition todata obtained from various log files, this performance-relatedinformation can include values for performance metrics obtained throughan application programming interface (API) provided as part of thevSphere HypervisorTM system distributed by VMware, Inc. of Palo Alto,Calif. For example, these performance metrics can include: (1)CPU-related performance metrics; (2) disk-related performance metrics;(3) memory-related performance metrics; (4) network-related performancemetrics; (5) energy-usage statistics; (6) data-traffic-relatedperformance metrics; (7) overall system availability performancemetrics; (8) cluster-related performance metrics; and (9) virtualmachine performance statistics. Such performance metrics are describedin U.S. patent application Ser. No. 14/167,316, entitled “CORRELATIONFOR USER-SELECTED TIME RANGES OF VALUES FOR PERFORMANCE METRICS OFCOMPONENTS IN AN INFORMATION-TECHNOLOGY ENVIRONMENT WITH LOG DATA FROMTHAT INFORMATION-TECHNOLOGY ENVIRONMENT”, filed on 29 Jan. 2014, andwhich is hereby incorporated by reference in its entirety for allpurposes.

To facilitate retrieving information of interest from performance dataand log files, the SPLUNK® APP FOR VMWARE® provides pre-specifiedschemas for extracting relevant values from different types ofperformance-related event data, and also enables a user to define suchschemas.

The SPLUNK® APP FOR VMWARE® additionally provides various visualizationsto facilitate detecting and diagnosing the root cause of performanceproblems. For example, one such visualization is a “proactive monitoringtree” that enables a user to easily view and understand relationshipsamong various factors that affect the performance of a hierarchicallystructured computing system. This proactive monitoring tree enables auser to easily navigate the hierarchy by selectively expanding nodesrepresenting various entities (e.g., virtual centers or computingclusters) to view performance information for lower-level nodesassociated with lower-level entities (e.g., virtual machines or hostsystems). Example node-expansion operations are illustrated in FIG. 9C,wherein nodes 933 and 934 are selectively expanded. Note that nodes931-939 can be displayed using different patterns or colors to representdifferent performance states, such as a critical state, a warning state,a normal state or an unknown/offline state. The ease of navigationprovided by selective expansion in combination with the associatedperformance-state information enables a user to quickly diagnose theroot cause of a performance problem. The proactive monitoring tree isdescribed in further detail in U.S. patent application Ser. No.14/253,490, entitled “PROACTIVE MONITORING TREE WITH SEVERITY STATESORTING”, filed on 15 Apr. 2014, and U.S. patent application Ser. No.14/812,948, also entitled “PROACTIVE MONITORING TREE WITH SEVERITY STATESORTING”, filed on 29 Jul. 2015, each of which is hereby incorporated byreference in its entirety for all purposes.

The SPLUNK® APP FOR VMWARE ® also provides a user interface that enablesa user to select a specific time range and then view heterogeneous datacomprising events, log data, and associated performance metrics for theselected time range. For example, the screen illustrated in FIG. 9Ddisplays a listing of recent “tasks and events” and a listing of recent“log entries” for a selected time range above a performance-metric graphfor “average CPU core utilization” for the selected time range. Notethat a user is able to operate pull-down menus 942 to selectivelydisplay different performance metric graphs for the selected time range.This enables the user to correlate trends in the performance-metricgraph with corresponding event and log data to quickly determine theroot cause of a performance problem. This user interface is described inmore detail in U.S. patent application Ser. No. 14/167,316, entitled“CORRELATION FOR USER-SELECTED TIME RANGES OF VALUES FOR PERFORMANCEMETRICS OF COMPONENTS IN AN INFORMATION-TECHNOLOGY ENVIRONMENT WITH LOGDATA FROM THAT INFORMATION-TECHNOLOGY ENVIRONMENT”, filed on 29 Jan.2014, and which is hereby incorporated by reference in its entirety forall purposes.

2.12. Cloud-Based System Overview

The example data intake and query system 108 described in reference toFIG. 2 comprises several system components, including one or moreforwarders, indexers, and search heads. In some environments, a user ofa data intake and query system 108 may install and configure, oncomputing devices owned and operated by the user, one or more softwareapplications that implement some or all of these system components. Forexample, a user may install a software application on server computersowned by the user and configure each server to operate as one or more ofa forwarder, an indexer, a search head, etc. This arrangement generallymay be referred to as an “on-premises” solution. That is, the system 108is installed and operates on computing devices directly controlled bythe user of the system. Some users may prefer an on-premises solutionbecause it may provide a greater level of control over the configurationof certain aspects of the system (e.g., security, privacy, standards,controls, etc.). However, other users may instead prefer an arrangementin which the user is not directly responsible for providing and managingthe computing devices upon which various components of system 108operate.

In one embodiment, to provide an alternative to an entirely on-premisesenvironment for system 108, one or more of the components of a dataintake and query system instead may be provided as a cloud-basedservice. In this context, a cloud-based service refers to a servicehosted by one more computing resources that are accessible to end usersover a network, for example, by using a web browser or other applicationon a client device to interface with the remote computing resources. Forexample, a service provider may provide a cloud-based data intake andquery system by managing computing resources configured to implementvarious aspects of the system (e.g., forwarders, indexers, search heads,etc.) and by providing access to the system to end users via a network.Typically, a user may pay a subscription or other fee to use such aservice. Each subscribing user of the cloud-based service may beprovided with an account that enables the user to configure a customizedcloud-based system based on the user's preferences.

FIG. 10 illustrates a block diagram of an example cloud-based dataintake and query system. Similar to the system of FIG. 2, the networkedcomputer system 1000 includes input data sources 202 and forwarders 204.These input data sources and forwarders may be in a subscriber's privatecomputing environment. Alternatively, they might be directly managed bythe service provider as part of the cloud service. In the example system1000, one or more forwarders 204 and client devices 1002 are coupled toa cloud-based data intake and query system 1006 via one or more networks1004. Network 1004 broadly represents one or more LANs, WANs, cellularnetworks, intranetworks, internetworks, etc., using any of wired,wireless, terrestrial microwave, satellite links, etc., and may includethe public Internet, and is used by client devices 1002 and forwarders204 to access the system 1006. Similar to the system of 108, each of theforwarders 204 may be configured to receive data from an input sourceand to forward the data to other components of the system 1006 forfurther processing.

In an embodiment, a cloud-based data intake and query system 1006 maycomprise a plurality of system instances 1008. In general, each systeminstance 1008 may include one or more computing resources managed by aprovider of the cloud-based system 1006 made available to a particularsubscriber. The computing resources comprising a system instance 1008may, for example, include one or more servers or other devicesconfigured to implement one or more forwarders, indexers, search heads,and other components of a data intake and query system, similar tosystem 108. As indicated above, a subscriber may use a web browser orother application of a client device 1002 to access a web portal orother interface that enables the subscriber to configure an instance1008.

Each of the components of a system 108 (e.g., forwarders, indexers andsearch heads) may at times refer to various configuration files storedlocally at each component. Embodiments described herein provide acentral repository for maintaining configuration files for thecomponents of a system 108. Various components of a system 108 (e.g.,search heads) are able to receive changes to the configuration filesthrough a search head interface or a central management interface. Othercomponents of a system 108 (e.g., indexers and forwarders) are able toreceive changes to the configuration files from the central repository.These configuration files typically may involve some level of userconfiguration to accommodate particular types of data a user desires toanalyze and to account for other user preferences. However, in acloud-based service context, users typically may not have direct accessto the underlying computing resources implementing the various systemcomponents (e.g., the computing resources comprising each systeminstance 1008) and may desire to make such configurations indirectly,for example, using one or more web-based interfaces. Thus, thetechniques and systems described herein for providing user interfacesthat enable a user to configure source type definitions are applicableto both on-premises and cloud-based service contexts, or somecombination thereof (e.g., a hybrid system where both an on-premisesenvironment such as SPLUNK® ENTERPRISE and a cloud-based environmentsuch as SPLUNK CLOUDTM are centrally visible).

2.13. Searching Externally Archived Data

FIG. 11 shows a block diagram of an example of a data intake and querysystem 108 that provides transparent search facilities for data systemsthat are external to the data intake and query system. Such facilitiesare available in the HUNK® system provided by Splunk Inc. of SanFrancisco, Calif. HUNK® represents an analytics platform that enablesbusiness and IT teams to rapidly explore, analyze, and visualize data inHadoop and NoSQL data stores.

The search head 210 of the data intake and query system receives searchrequests from one or more client devices 1104 over network connections1120. As discussed above, the data intake and query system 108 mayreside in an enterprise location, in the cloud, etc. FIG. 11 illustratesthat multiple client devices 1104 a, 1104 b, . . . , 1104 n maycommunicate with the data intake and query system 108. The clientdevices 1104 may communicate with the data intake and query system usinga variety of connections. For example, one client device in FIG. 11 isillustrated as communicating over an Internet (Web) protocol, anotherclient device is illustrated as communicating via a command lineinterface, and another client device is illustrated as communicating viaa system developer kit (SDK).

The search head 210 analyzes the received search request to identifyrequest parameters. If a search request received from one of the clientdevices 1104 references an index maintained by the data intake and querysystem, then the search head 210 connects to one or more indexers 206 ofthe data intake and query system for the index referenced in the requestparameters. That is, if the request parameters of the search requestreference an index, then the search head accesses the data in the indexvia the indexer. The data intake and query system 108 may include one ormore indexers 206, depending on system access resources andrequirements. As described further below, the indexers 206 retrieve datafrom their respective local data stores 208 as specified in the searchrequest. The indexers and their respective data stores can comprise oneor more storage devices and typically reside on the same system, thoughthey may be connected via a local network connection.

If the request parameters of the received search request reference anexternal data collection, which is not accessible to the indexers 206 orunder the management of the data intake and query system, then thesearch head 210 can access the external data collection through anExternal Result Provider (ERP) process 1110. An external data collectionmay be referred to as a “virtual index” (plural, “virtual indices”). AnERP process provides an interface through which the search head 210 mayaccess virtual indices.

Thus, a search reference to an index of the system relates to a locallystored and managed data collection. In contrast, a search reference to avirtual index relates to an externally stored and managed datacollection, which the search head may access through one or more ERPprocesses 1110, 1112. FIG. 11 shows two ERP processes 1110, 1112 thatconnect to respective remote (external) virtual indices, which areindicated as a Hadoop or another system 1114 (e.g., Amazon S3, AmazonEMR, other Hadoop Compatible File Systems (HCFS), etc.) and a relationaldatabase management system (RDBMS) 1116. Other virtual indices mayinclude other file organizations and protocols, such as Structured QueryLanguage (SQL) and the like. The ellipses between the ERP processes1110, 1112 indicate optional additional ERP processes of the data intakeand query system 108. An ERP process may be a computer process that isinitiated or spawned by the search head 210 and is executed by thesearch data intake and query system 108. Alternatively or additionally,an ERP process may be a process spawned by the search head 210 on thesame or different host system as the search head 210 resides.

The search head 210 may spawn a single ERP process in response tomultiple virtual indices referenced in a search request, or the searchhead may spawn different ERP processes for different virtual indices.Generally, virtual indices that share common data configurations orprotocols may share ERP processes. For example, all search queryreferences to a Hadoop file system may be processed by the same ERPprocess, if the ERP process is suitably configured. Likewise, all searchquery references to an SQL database may be processed by the same ERPprocess. In addition, the search head may provide a common ERP processfor common external data source types (e.g., a common vendor may utilizea common ERP process, even if the vendor includes different data storagesystem types, such as Hadoop and SQL). Common indexing schemes also maybe handled by common ERP processes, such as flat text files or Weblogfiles.

The search head 210 determines the number of ERP processes to beinitiated via the use of configuration parameters that are included in asearch request message. Generally, there is a one-to-many relationshipbetween an external results provider “family” and ERP processes. Thereis also a one-to-many relationship between an ERP process andcorresponding virtual indices that are referred to in a search request.For example, using RDBMS, assume two independent instances of such asystem by one vendor, such as one RDBMS for production and another RDBMSused for development. In such a situation, it is likely preferable (butoptional) to use two ERP processes to maintain the independent operationas between production and development data. Both of the ERPs, however,will belong to the same family, because the two RDBMS system types arefrom the same vendor.

The ERP processes 1110, 1112 receive a search request from the searchhead 210. The search head may optimize the received search request forexecution at the respective external virtual index. Alternatively, theERP process may receive a search request as a result of analysisperformed by the search head or by a different system process. The ERPprocesses 1110, 1112 can communicate with the search head 210 viaconventional input/output routines (e.g., standard in/standard out,etc.). In this way, the ERP process receives the search request from aclient device such that the search request may be efficiently executedat the corresponding external virtual index.

The ERP processes 1110, 1112 may be implemented as a process of the dataintake and query system. Each ERP process may be provided by the dataintake and query system, or may be provided by process or applicationproviders who are independent of the data intake and query system. Eachrespective ERP process may include an interface application installed ata computer of the external result provider that ensures propercommunication between the search support system and the external resultprovider. The ERP processes 1110, 1112 generate appropriate searchrequests in the protocol and syntax of the respective virtual indices1114, 1116, each of which corresponds to the search request received bythe search head 210. Upon receiving search results from theircorresponding virtual indices, the respective ERP process passes theresult to the search head 210, which may return or display the resultsor a processed set of results based on the returned results to therespective client device.

Client devices 1104 may communicate with the data intake and querysystem 108 through a network interface 1120, e.g., one or more LANs,WANs, cellular networks, intranetworks, and/or internetworks using anyof wired, wireless, terrestrial microwave, satellite links, etc., andmay include the public Internet.

The analytics platform utilizing the External Result Provider processdescribed in more detail in U.S. Pat. No. 8,738,629, entitled “EXTERNALRESULT PROVIDED PROCESS FOR RETRIEVING DATA STORED USING A DIFFERENTCONFIGURATION OR PROTOCOL”, issued on 27 May 2014, U.S. Pat. No.8,738,587, entitled “PROCESSING A SYSTEM SEARCH REQUEST BY RETRIEVINGRESULTS FROM BOTH A NATIVE INDEX AND A VIRTUAL INDEX”, issued on 25 Jul.2013, U.S. patent application Ser. No. 14/266,832, entitled “PROCESSINGA SYSTEM SEARCH REQUEST ACROSS DISPARATE DATA COLLECTION SYSTEMS”, filedon 1 May 2014, and U.S. patent application Ser. No. 14/449,144, entitled“PROCESSING A SYSTEM SEARCH REQUEST INCLUDING EXTERNAL DATA SOURCES”,filed on 31 Jul. 2014, each of which is hereby incorporated by referencein its entirety for all purposes.

2.13.1. ERP Process Features

The ERP processes described above may include two operation modes: astreaming mode and a reporting mode. The ERP processes can operate instreaming mode only, in reporting mode only, or in both modessimultaneously. Operating in both modes simultaneously is referred to asmixed mode operation. In a mixed mode operation, the ERP at some pointcan stop providing the search head with streaming results and onlyprovide reporting results thereafter, or the search head at some pointmay start ignoring streaming results it has been using and only usereporting results thereafter.

The streaming mode returns search results in real time, with minimalprocessing, in response to the search request. The reporting modeprovides results of a search request with processing of the searchresults prior to providing them to the requesting search head, which inturn provides results to the requesting client device. ERP operationwith such multiple modes provides greater performance flexibility withregard to report time, search latency, and resource utilization.

In a mixed mode operation, both streaming mode and reporting mode areoperating simultaneously. The streaming mode results (e.g., the raw dataobtained from the external data source) are provided to the search head,which can then process the results data (e.g., break the raw data intoevents, timestamp it, filter it, etc.) and integrate the results datawith the results data from other external data sources, and/or from datastores of the search head. The search head performs such processing andcan immediately start returning interim (streaming mode) results to theuser at the requesting client device; simultaneously, the search head iswaiting for the ERP process to process the data it is retrieving fromthe external data source as a result of the concurrently executingreporting mode.

In some instances, the ERP process initially operates in a mixed mode,such that the streaming mode operates to enable the ERP quickly toreturn interim results (e.g., some of the raw or unprocessed datanecessary to respond to a search request) to the search head, enablingthe search head to process the interim results and begin providing tothe client or search requester interim results that are responsive tothe query. Meanwhile, in this mixed mode, the ERP also operatesconcurrently in reporting mode, processing portions of raw data in amanner responsive to the search query. Upon determining that it hasresults from the reporting mode available to return to the search head,the ERP may halt processing in the mixed mode at that time (or somelater time) by stopping the return of data in streaming mode to thesearch head and switching to reporting mode only. The ERP at this pointstarts sending interim results in reporting mode to the search head,which in turn may then present this processed data responsive to thesearch request to the client or search requester. Typically the searchhead switches from using results from the ERP' s streaming mode ofoperation to results from the ERP' s reporting mode of operation whenthe higher bandwidth results from the reporting mode outstrip the amountof data processed by the search head in the ]streaming mode of ERPoperation.

A reporting mode may have a higher bandwidth because the ERP does nothave to spend time transferring data to the search head for processingall the raw data. In addition, the ERP may optionally direct anotherprocessor to do the processing.

The streaming mode of operation does not need to be stopped to gain thehigher bandwidth benefits of a reporting mode; the search head couldsimply stop using the streaming mode results—and start using thereporting mode results—when the bandwidth of the reporting mode hascaught up with or exceeded the amount of bandwidth provided by thestreaming mode. Thus, a variety of triggers and ways to accomplish asearch head's switch from using streaming mode results to usingreporting mode results may be appreciated by one skilled in the art.

The reporting mode can involve the ERP process (or an external system)performing event breaking, time stamping, filtering of events to matchthe search query request, and calculating statistics on the results. Theuser can request particular types of data, such as if the search queryitself involves types of events, or the search request may ask forstatistics on data, such as on events that meet the search request. Ineither case, the search head understands the query language used in thereceived query request, which may be a proprietary language. One examplequery language is Splunk Processing Language (SPL) developed by theassignee of the application, Splunk Inc. The search head typicallyunderstands how to use that language to obtain data from the indexers,which store data in a format used by the SPLUNK® Enterprise system.

The ERP processes support the search head, as the search head is notordinarily configured to understand the format in which data is storedin external data sources such as Hadoop or SQL data systems. Rather, theERP process performs that translation from the query submitted in thesearch support system's native format (e.g., SPL if SPLUNK® ENTERPRISEis used as the search support system) to a search query request formatthat will be accepted by the corresponding external data system. Theexternal data system typically stores data in a different format fromthat of the search support system's native index format, and it utilizesa different query language (e.g., SQL or MapReduce, rather than SPL orthe like).

As noted, the ERP process can operate in the streaming mode alone. Afterthe ERP process has performed the translation of the query request andreceived raw results from the streaming mode, the search head canintegrate the returned data with any data obtained from local datasources (e.g., native to the search support system), other external datasources, and other ERP processes (if such operations were required tosatisfy the terms of the search query). An advantage of mixed modeoperation is that, in addition to streaming mode, the ERP process isalso executing concurrently in reporting mode. Thus, the ERP process(rather than the search head) is processing query results (e.g.,performing event breaking, timestamping, filtering, possibly calculatingstatistics if required to be responsive to the search query request,etc.). It should be apparent to those skilled in the art that additionaltime is needed for the ERP process to perform the processing in such aconfiguration. Therefore, the streaming mode will allow the search headto start returning interim results to the user at the client devicebefore the ERP process can complete sufficient processing to startreturning any search results. The switchover between streaming andreporting mode happens when the ERP process determines that theswitchover is appropriate, such as when the ERP process determines itcan begin returning meaningful results from its reporting mode.

The operation described above illustrates the source of operationallatency: streaming mode has low latency (immediate results) and usuallyhas relatively low bandwidth (fewer results can be returned per unit oftime). In contrast, the concurrently running reporting mode hasrelatively high latency (it has to perform a lot more processing beforereturning any results) and usually has relatively high bandwidth (moreresults can be processed per unit of time). For example, when the ERPprocess does begin returning report results, it returns more processedresults than in the streaming mode, because, e.g., statistics only needto be calculated to be responsive to the search request. That is, theERP process doesn't have to take time to first return raw data to thesearch head. As noted, the ERP process could be configured to operate instreaming mode alone and return just the raw data for the search head toprocess in a way that is responsive to the search request.Alternatively, the ERP process can be configured to operate in thereporting mode only. Also, the ERP process can be configured to operatein streaming mode and reporting mode concurrently, as described, withthe ERP process stopping the transmission of streaming results to thesearch head when the concurrently running reporting mode has caught upand started providing results. The reporting mode does not require theprocessing of all raw data that is responsive to the search queryrequest before the ERP process starts returning results; rather, thereporting mode usually performs processing of chunks of events andreturns the processing results to the search head for each chunk.

For example, an ERP process can be configured to merely return thecontents of a search result file verbatim, with little or no processingof results. That way, the search head performs all processing (such asparsing byte streams into events, filtering, etc.). The ERP process canbe configured to perform additional intelligence , such as analyzing thesearch request and handling all the computation that a native searchindexer process would otherwise perform. In this way, the configured ERPprocess provides greater flexibility in features while operatingaccording to desired preferences, such as response latency and resourcerequirements.

3.0. Central Repository for Configuration Files

Described herein are techniques for maintaining configuration files forcomponents of a distributed system in a central repository. Componentsof distributed system (e.g., system 108 of FIG. 1) are configurable andmay refer to configuration files stored locally at each component. Theseconfiguration files typically may involve some level of userconfiguration to accommodate particular types of data a user desires toanalyze and to account for other user preferences. Embodiments describedherein provide a central repository for maintaining configuration filesfor the components of a distributed system.

In various embodiments, a computer-implemented method is provided forconfiguring a distributed computer system comprising a plurality ofnodes of a plurality of node classes. Configuration files for aplurality of nodes of each of the plurality of node classes are storedin a central repository. The configuration files include informationrepresenting a desired system state of the distributed computer system,and the distributed computer system operates to keep an actual systemstate of the distributed computer system consistent with the desiredsystem state. The plurality of node classes includes forwarder nodes forreceiving data from an input source, indexer nodes for indexing thedata, and search head nodes for searching the data. Responsive toreceiving changes to the configuration files, the changes are propagatedto nodes of the plurality of nodes impacted by the changes based on anode class of the nodes impacted by the changes.

In various embodiments, asynchronous status notifications are receivedfrom nodes representative of an actual state of the nodes. Statusinformation representative of the actual state of the distributedcomputer system is provided and progress in achieving consistency withthe desired state of the distributed computer system.

In various embodiments, the central repository includes a commonconfiguration file sub-repository for maintaining configuration filescommon to the plurality of nodes of the plurality of node classes, aforwarder configuration sub-repository for maintaining configurationfiles for the forwarder nodes, an indexer configuration sub-repositoryfor maintaining configuration files for the indexer nodes, and a searchhead configuration sub-repository for maintaining configuration filesfor the search head nodes. In various embodiments, configuration filesfor specific nodes are stored in a particular portion of the centralrepository, the configuration files for the specific nodes allowing forarbitrary configuration changes to the specific nodes. In variousembodiments, the central repository is configured to maintain aplurality of versions of the configuration files.

In various embodiments, a transaction comprising a change to theconfiguration files is received, wherein the transaction furthercomprises acceptance criteria for the change to the configuration files.

In various embodiments, asynchronous status notifications are receivedfrom nodes representative of an actual state of the nodes. Acceptancecriteria are compared to the asynchronous status notifications. It isthen determined whether the acceptance criteria has been satisfied basedat least in part on the asynchronous status notifications.

In various embodiments, a transaction comprising a change to theconfiguration files is received. It is determined whether thetransaction conflicts with the configuration files. Responsive to thetransaction not conflicting with the configuration files, thetransaction is merged into the configuration files such that the desiredsystem state is updated.

In various embodiments, a first transaction comprising a first change tothe configuration files is received. A second transaction comprising asecond change to the configuration files is received, wherein the secondtransaction is received subsequent to the first transaction. The firstchange is merged into the configuration files. It is determined whetherthe first change conflicts with the second change. Responsive to thefirst change conflicting with the second change, the second change isnot merged into the configuration files and a notification that thesecond change has not been merged into the configuration files isprovided.

In various embodiments, the changes to the configuration files arereceived from a single management node of the distributed computersystem. In various embodiments, the changes are made to the plurality ofnodes via a representational state transfer (REST) call.

In various embodiments, responsive to receiving changes in theconfiguration files impacting the search nodes, the changes arepropagated to the search nodes from the central repository. In variousembodiments, responsive to receiving changes in the configuration filesat a search node, the changes are propagated to the central repositoryfrom the search node.

In various embodiments, responsive to changes in the configuration filesimpacting the index nodes, the changes are propagated to the index nodesfrom the central repository. In various embodiments, responsive tochanges in the configuration files impacting the forwarder nodes, thechanges are propagated to the forwarder nodes from the centralrepository.

In various embodiments, responsive to changes in the configuration filescomprising a stanza change, a delta of the stanza change is propagatedto the nodes of the plurality of nodes impacted by the changes.

In various embodiments, the changes are received at a search head node.The search head node pulls a configuration file from a search headcaptain node. The changes are merged with the configuration file. Theconfiguration file with the changes is pushed to the search head captainnode, wherein the configuration file is updated to include the changes.The configuration file is then pushed to the central repository.

In various embodiments, the changes are propagated from the centralrepository to a mediator node of the node class impacted by the changes.The changes are then propagated from the mediator node to the nodesimpacted by the changes. In various embodiments, the changes arepropagated from the central repository to a search head captain node ofthe search head nodes. The changes are then propagated from the searchhead captain node to the search head nodes impacted by the changes. Invarious embodiments, the changes are propagated from the centralrepository to a cluster master of the indexer nodes. The changes arethen propagated from the cluster master to the indexer nodes impacted bythe changes. In various embodiments, portions of the nodes of theplurality of nodes impacted by the changes are reloaded.

FIG. 18A illustrates a block diagram of an example distributed computersystem 1800 for maintaining a central repository 1812 of configurationfiles across nodes of different node classes (e.g., search head nodes,indexer nodes, and forwarder nodes), in accordance with someembodiments. In one embodiment, central repository 1812 is locatedwithin a management node 1810 of distributed computer system 1800. Forexample, central repository 1812 may include an authoritative version ofthe configuration files for nodes of a plurality of node classesincluding search head cluster 1820, indexer cluster 1822, and forwarders1824. It should be appreciated that the term “node” as used hereinrefers to components of search head cluster 1820, indexer cluster 1822,and forwarders 1824 (e.g., search heads, indexers, and/or forwarders).In an embodiment, nodes of distributed computer system 1800 arecommunicatively coupled via networks 1815. Networks 1815 broadlyrepresent one or more LANs, WANs, cellular networks (e.g., LTE, HSPA,3G, and other cellular technologies), and/or networks using any ofwired, wireless, terrestrial microwave, or satellite links, and mayinclude the public Internet.

The configuration files of distributed computer system 1800 are used todetermine the behavior and functionality of the nodes. For example,configuration files include information regarding system settings, userinterface settings, authentication and authorization information, indexmappings and settings, deployment and cluster configurations, knowledgeobjects and saved search, other customizations, etc. In one embodiment,configuration files are identified by the .conf extension appended tothe end of the file name. It should be appreciated that someconfigurations are uniform across all node classes, while otherconfigurations are available for particular node classes, or forparticular nodes of a node class.

Central repository 1812 provides a centralized location for maintainingthe configuration files for all nodes of distributed computer system1800, allowing for unification of the maintenance of the configurationfiles, and consistent management of updates and changes to theconfiguration files. Updates to the configuration files are received atmanagement node 1810 (e.g., via an administrator user interface 1805)and are propagated to the nodes of search head cluster 1820, indexercluster 1822, and forwarders 1824. In one embodiment, as describedbelow, updates to the configuration files of search head cluster 1820can also be received at the search head nodes, with these updates beingpropagated to central repository 1812.

In accordance with various embodiments, changes made to configurationfiles of central repository 1812 are propagated to nodes of therespective node classes via a mediator node 1830 (shown in FIG. 18A asmediator nodes 1830 a-c). The functionality of a mediator node isdependent on the node class, and may include additional functionalitybeyond the propagation of configuration files. For example, the mediatornode may be a search head cluster deployer or a captain for search headnodes, a cluster master or a coordinator for indexer nodes, or adeployment server for forwarder nodes

The configuration files of central repository 1812 represent a desiredsystem state of distributed computer system 1800. Each node ofdistributed computer system 1800 includes a local data store for storingits configuration files. The configuration files of central repository1812 are the authoritative version of the configuration files, such thateach node operates to maintain consistency with the configuration filesof central repository 1812. In accordance with various embodiments,changes to the configuration files of central repository 1812 arereplicated to nodes of distributed computer system 1800. The desiredsystem state is distinguished from the actual system state, which isrepresentative of the actual configurations of the nodes of the system,including those that have not yet synchronized with the centralrepository. For example, it should be appreciated that the desired statemay take time to materialize in the nodes, and that various mechanismsmay be utilized to provide status and visibility into the actual currentstate and the progress of deploying the desired state.

Since all configurations files are maintained in a single location, thecentral repository 1812 allows for simple backups, versioning, andmigration. By maintaining the configuration files in the authoritativestate (e.g., the desired state), central repository 1812 holds aneventually consistent view of all configurations in the distributedcomputer system 1800. The central repository 1812 replicates theconfigurations to the nodes, and, in the case of search head nodes, theconfiguration files can be replicated to central repository 1812.

FIG. 18B illustrates a block diagram of an example organization of acentral repository 1812 of configuration files across nodes of differentnode classes, in accordance with some embodiments. While someconfiguration files of nodes of distributed computer system 1800 arespecific to particular node classes, in some embodiments, certainconfiguration files may be common to all node classes. Accordingly,common configuration files may be maintained in common repository 1910,and node class specific configuration files may be maintained in nodespecific portions of central repository 1812. As illustrated, searchhead configuration repository 1920 maintains the configuration files forsearch head nodes, indexer configuration repository 1922 maintains theconfiguration files for indexer nodes, and forwarder configurationrepository 1924 maintains the configuration files for forwarder nodes.

In various embodiments, the node class specific portions of the centralrepository 1812 may be further organized according to sub-portions ofthe node class specific repository. For example, as shown in FIG. 18B,forwarder repository 1924 is communicatively coupled to forwardersub-repository 1932 and forwarder sub-repository 1934. Thesesub-repositories operate to share common configurations to allforwarders, while also maintaining specific configuration files forrespective subsets of forwarder nodes. For example, different serverclasses may have different configuration files for forwarder nodes. Thesub-repositories of central repository 1812 maintain configuration filesfor the respective subsets of forwarder nodes, and propagate thoseconfiguration files to the respective subsets of the forwarder nodes. Itshould be appreciated that central repository 1812 may be logicallyorganized using any number of sub-repositories, as well as furtherrepositories dependent on higher level repositories, depending on theorganization of the components of distributed computer system 1800.

With reference to FIG. 18A, in various embodiments, for some portions ofcentral repository 1812, such as the search head nodes of search headcluster 1820, two-way replication can be used, as changes to theconfiguration files may also be received directly at the search nodes.In some embodiments, for other portions of central repository 1812, suchas forwarder nodes and indexer nodes, one-way replication, outward fromthe central repository 1812 is available. For example, changes to theforwarder nodes and indexer nodes may not be available directly,requiring such changes to be made at management node 1810.

FIG. 19 illustrates a data flow diagram 1900 of the propagation ofconfiguration files between a central repository 1812 of configurationfiles and nodes of the example distributed computer system 1800, inaccordance with some embodiments. In one embodiment, central repository1812 is partitioned into different sub-repositories for each node class.As illustrated, search head configuration repository 1920 maintains theconfiguration files for nodes of search head cluster 1820, indexerconfiguration repository 1922 maintains the configuration files fornodes of indexer cluster 1822, and forwarder configuration repository1924 maintains the configuration files for nodes of forwarders 1824.

In accordance with various embodiments, changes made to configurationfiles of central repository 1812 are propagated to nodes of therespective node classes via a mediator node. As described above, thefunctionality of the mediator node is dependent on the node class, andmay include additional functionality beyond the propagation ofconfiguration files. For example, the mediator node may be a search headcluster deployer or a captain for search head nodes, a cluster master ora coordinator for indexer nodes, or a deployment server for forwardernodes.

With reference to search head cluster 1820, in various embodiments,changes in configuration files of the search head nodes are replicatedto other head nodes through captain 1935. For example, in someembodiments, search head cluster replication utilizes a search headconfiguration repository located at captain 1935. In variousembodiments, the search head configuration repository manages deltas andcheckpoints/snapshots for each node of search head cluster 1820. Achange made in search head repository 1920 is propagated to captain1935, which then propagates the changes to the nodes of search headcluster 1820. As described above, in various embodiments, captain 1935can be another type of mediator, such as a search head cluster deployer.In other embodiments, the changes can be propagated directly to thenodes of the search head cluster 1820.

It should be appreciated that changes to the configuration files canalso be received via search UI 1960. When changes are made to a searchhead cluster node (e.g., via a REST call), the node's configurationfiles are updated. These updates can be received either at centralrepository 1812 or directly at a search head node of search head cluster1820, wherein the replication procedures across the nodes is the same.In some embodiments, the changes themselves are recorded as deltas(e.g., in a file called ops.json). Search head cluster nodesasynchronously replicate their configuration files to the captain bytransmitting deltas. For example, this is accomplished by first“pulling” the search head configuration repository from the captain1935, then merging the local changes, then pushing the merged deltasback to the captain 1935. The initial pull request will download asnapshot from the captain 1935 to establish a baseline. This snapshotoverwrites the local configuration file. Therefore, nodes begin byaccessing the search head configuration repository from the captain, andthereafter pull and push deltas. In various embodiments, snapshots arealso maintained on the search head cluster nodes in the event that nodeis ever elected as a captain (as the search head cluster allows fordynamic election of the captain should the captain fail or the clusterbecome partitioned).

It should be appreciated that since the configuration file pull/pushhappens periodically and/or asynchronously, there is no requirement forcontinuous connectivity between search head nodes and the captain 1935.Pull and push operations can proceed whenever the captain is visible. Inthe intervening times, the members can receive REST create, read, updateand delete (crud) commands, which may buffered in deltas. This allowsthe search head nodes to operate indefinitely without being able to seethe captain 1935. In other words, the search head nodes can remainavailable if they are not able to access captain 1935. In variousembodiments, a partition divides search head cluster 1820 into amajority and a minority. The captain 1935 will always live in themajority partition (or will be re-elected in the majority, sincemajority is a requirement for captainship). Search head nodes in themajority partition will continue to replicate their changes at theperiodic rate. Search head nodes in the minority partition buffer theirindividual changes and will merge them when the partition is healed.Therefore, availability has different meaning depending on if aparticular search head node can see the captain 1935 or not.

Examples of functionality that enables replication of configurationfiles among nodes of a search head cluster are described in U.S. patentapplication Ser. No. 14/448,919, entitled “CONFIGURATION REPLICATION INA SEARCH HEAD CLUSTER”, filed on 31 Jul. 2014, and which is herebyincorporated by reference in its entirety for all purposes.

With reference to indexer cluster 1822, in various embodiments, changesin configuration files of the indexer nodes are replicated to otherindexer nodes through coordinator 1945. For example, in someembodiments, indexer node replication utilizes an indexer configurationrepository located at coordinator 1945. In various embodiments, theindexer configuration repository manages deltas andcheckpoints/snapshots for each node of indexer cluster 1822. A changemade in indexer repository 1922 is propagated to coordinator 1945, whichthen propagates the changes to the nodes of indexer cluster 1822. Asdescribed above, in various embodiments, coordinator 1945 can be anothertype of mediator, such as a cluster master. In other embodiments, thechanges can be propagated directly to the nodes of the indexer cluster1822.

With reference to forwarders 1824, in various embodiments, changes inconfiguration files of the indexer nodes are replicated to otherforwarder nodes through forwarder 1955. For example, in someembodiments, forwarder node replication utilizes an forwarderconfiguration repository located at forwarder 1955. In variousembodiments, the forwarder configuration repository manages deltas andcheckpoints/snapshots for each node of forwarders 1824. A change made inforwarder repository 1924 is propagated to coordinator 1945, which thenpropagates the changes to the nodes of forwarders 1824. As describedabove, in various embodiments, coordinator 1945 can be another type ofmediator, such as a deployment server. In other embodiments, the changescan be propagated directly to the nodes of the forwarders 1824.

In various embodiments, propagation of configuration changes may triggerreloads of impacted systems or subsystems.

FIG. 20 illustrates a data flow diagram 2000 of the asynchronousfeedback of the configuration status of nodes of the example distributedcomputer system 1800, in accordance with some embodiments. Uponreceiving a change in the configuration files, a node transmits amessage indicating the status of the change (e.g., that theconfiguration was updated successfully, or that the configuration failedfor a stated reason). The messages are transmitted asynchronously,allowing for nodes to notify of their respective configuration status atthe time the node is able to receive the configuration change.

In various embodiments, as described above, it is the responsibility ofthe configuration replication operation of a node class to inform thenode itself that changes to configuration have arrived. In oneembodiment, this can take the form of an observer/observable or callbackbased API to configuration files. For example, the present embodimentdoes not require a sideband channel to signal the node thatconfiguration has changed. Rather, change listeners on the node areautomatically invoked when the local configuration file is updated. Inone embodiment, real-time interactive feedback is provided toadministrator UI 1805, regarding the progress of configuration changes(e.g., rolling out an index). The feedback can be provided, for example,via a message bus, or through updating observable collections.

As shown in FIG. 20, asynchronous notifications 2005, 2010 and 2015 arecommunicated to administrator UI 1805 from nodes of distributed computersystem 1800. In one embodiment, a publish/subscribe (pub/sub) stylemessage bus insures that messages are reliably delivered. It should beappreciated that the messages are used to provide feedback. The messagesinform the administrator UI 1805 (or other interested actors) as nodesactivate the changes that they receive from central repository 1812. Itshould be appreciated that changes to the central repository 1812 maygenerate observable events that the nodes act on. Therefore, anothermethod of receiving notification of change is through registering remotelistener callbacks for messages stored in the central repository. Thisprovides a higher level abstraction than the message bus, though themessage bus may be used beneath the API to implement the communication.

In one embodiment, message broker 2020 operates to communicateasynchronous notifications 2005, 2010 and 2015 to administrator UI 1805from nodes of distributed computer system 1800. For example, messagebroker 202 receives and stores asynchronous notifications 2005, 2010 and2015 intended for administrator UI 1805. In the event that administratorUI 1805 is unavailable, message broker 2020 acts as persistent storagefor asynchronous notifications 2005, 2010 and 2015. Upon theavailability of administrator 1805 to receive communications, messagebroker 2020 communicates asynchronous notifications 2005, 2010 and 2015to administrator UI 1805.

FIG. 21 illustrates a data flow diagram 2100 of the maintenance andpropagation of configuration files to specific nodes (e.g., snowflakenodes) of the example distributed computer system 1800, in accordancewith some embodiments. Similar to the example organization of centralrepository 1812 described in accordance with FIG. 18B, it may bedesirable to allow for arbitrary configuration changes to specificnodes. In various embodiments, creating and maintaining a separatesnowflake repository 2110 allows for the maintenance and propagation ofconfiguration files to members of the snowflake nodes.

As shown in FIG. 21, indexer cluster 1822 includes snowflake 2120. Itshould be appreciated that snowflake 2120 may include any number ofnodes of the indexer cluster 1822, and that these nodes receiveconfiguration changes from snowflake repository 2110. It should furtherbe appreciated that snowflake 2120 may include a mediator node (e.g., acoordinator) for propagating configuration changes to the nodes ofsnowflake 2120, or the changes may be propagated directly to the nodesof snowflake 2120. While FIG. 21 illustrates that snowflake 2120 residesin indexer cluster 1822, it should be appreciated that a snowflake canreside in any node class, and that there can be any number of snowflakesand corresponding snowflake repositories.

FIG. 22 illustrates a data flow diagram 2200 of an example propagationof configuration files to forwarder nodes, in accordance with someembodiments. As illustrated, forwarder node scalability is handled byhorizontally scaling the central repository 1812 into multiple instances(central repositories 1812 a, 1812 b and 1812 c). These instances arepeered and include the same configuration files within the respectivenode class repositories. By peering these instances, consistency acrosscentral repositories 1812 a, 1812 b and 1812 c is maintained. Loadbalancer 2230 balances the propagation of configuration files toforwarders 1824 a-d. Since central repositories 1812 a, 1812 b and 1812c include the same configuration files, forwarder 1824 a-d can receivethe configuration files from any of the peered central repositories.Load balancer 2230 dictates which instance of the central repositorythat a forwarder receives its configuration files from.

It should be appreciated that many different topologies are possible forsyncing replicated central repositories. For example, the centralrepositories can be replicated using a hub and spoke arrangement, a ringarrangement, a pyramid arrangement, or any other type of topology.

In other embodiments, the central repository can initialize itself froma deployment. For instance, all the information stored in the centralrepository exists on cluster nodes. Central repositories can be broughtup “from scratch” by starting a new central repository and connecting itto a deployment (e.g., a distributed computer system 1800). Thedeployment can continue to operate in the absence of any centralrepositories, although some management functions may not be available.Accordingly, the centralized repository does not introduce a point offailure. In some embodiments, high availability is achieved bymaintaining a standby central repository included a replicated versionof the active central repository. The same replication described abovefor replicating configuration files between nodes and the centralrepository can be used to replicate files between central repositories.The central repository is replicated so that the standby centralrepository is ready for active use if the active central repository goesdown.

In various embodiments, a multisite configuration of the distributedcomputing network is available by peering the central repositoriesacross the multiple locations. Each site has its own dynamic captain,and the sites are “peered” by their central repositories. Instead ofevery node in one site talking to the captain in another site, thecentral repositories consolidate the communication. In effect, each sitehas a separate search head cluster, but they are kept in sync. This isboth more efficient, and easier to reason about than a single searchhead cluster, spanning two sites, with a captain that lives only in onesite. In the event that the sites are disconnected and experience aconfiguration drift, the process of resolving the drift is greatlysimplified since only two nodes are involved in the “reconnect andresolve” process.

In various embodiments, configuration changes made at the centralrepository 1812 are received via administrator user interface 1805. Forexample, representational state transfer (REST) APIs are presented on asingle management node 1810 that maintains a view of the configurationstate of distributed computer system 1800. Many capabilities, such aslisting system objects, and validating proposed changes can be performedby operating on central repository 1812 representing the desired stateof the configuration files. The nodes operate to remain insynchronization with this desired state. For some node classes, such assearch head nodes, syncing can be triggered so that changes can “push”the cluster as soon as they occur. For other node classes, such asforwarder nodes, network topologies and other considerations maynecessitate the use of a “pull” model. In various embodiments, sometypes of configuration edit operations, such as simple stanza changes,are propagated as deltas. For other types of configuration editoperations, such as applications, the changes may be transmitted asbinary large objects (BLOBS).

For example, when a client asks the REST API of management node 1810 tolist some objects (e.g., the indexes), the REST API can return thisinformation without communicating with any nodes from the distributedcomputer system 1800. In other words, “list” or “read” operations willread from the configuration files of central repository 1812. In variousembodiments, management operations read from a trunk of the centralrepository 1812, where the trunk represents that committed changes tothe configuration files, thus representing the desired system state.

In various embodiments, changes made to the configuration of are firstmade to branches of the central repository 1812. A change is submittedto central repository 1812 in a transaction, where the transaction iseffectively a branch of the trunk of central repository 1812. The branchcan be read in a similar manner as the trunk to the administrator makingthe changes. However, since the branch represents a non-committedchange, other administrators do not see the branch. When a transactioncommits, it is determined whether there are conflicts between the trunkand the branch. If there are no conflicts, the branch is merged with thetrunk. In other words, if there are zero conflicts between branches,then the merge succeeds. If there were any conflicts (e.g., caused byanother transaction committing prior to this one), then the merge fails.In one embodiment, the branch remains open, and the administrator isinvited to resolve the conflicts.

FIG. 23 illustrates an example flow diagram for committing configurationchanges to the central repository, and for resolving conflicts, inaccordance with various embodiments. Consider the case when twoadministrators are concurrently making changes to central repository1812. For example, an Admin A creates 2325 an index named “foo,” but hasnot yet deployed the index. Admin A effectively creates a branch 2320 oftrunk 2310, where trunk 2310 represents the committed configurationfiles of central repository 1812. Concurrently, Admin B also creates2345 an index named “foo,” resulting in the creation of branch 2340.Admin A and Admin B are not able to see each other's changes until thechange is committed to the trunk. Admin A then commits their changeprior to Admin B attempting to commit the conflicting change, merging2330 branch 2320 into trunk 2310. When Admin B attempts to commit thechange, the merge 2350 will fail because the index with the same namehas already been created. For instance, the transaction will not bemerged with the trunk, and Admin B might be presented with a messagesuch as: “Index creation failed: an index named FOO already exists.”

In various embodiments, the following REST APIs are used to support thefollowing operations:

-   -   /services/transaction/        -   POST returns a transaction ID (same thing as a branch name).            Internally the repo is branched.        -   GET returns the list of transaction IDs    -   /services/transaction/<ID>        -   POST commits the transaction. Returns 409 Conflict on merge            failures caused by conflicts.        -   GET returns the status of the transaction:            -   [SUCCEEDED|OPEN|FAILED}            -   the list of confOps (edit operations) that have occurred                during the transaction, including operations that failed                to merge if the transaction is FAILED        -   DELETE removes the branch/transaction (only works if the            transaction <ID> is currently OPEN).    -   /services/clusterstate        -   GET returns the state of the cluster with respect to            transactions (branches) that have been successfully            committed (merged). This returns a map where the key is Node            ID, and the value is the transaction ID that the node is            currently “at”. This endpoint can be polled in order to            monitor the progress of a transaction.

All existing REST APIs that perform create, read, update, or delete(CRUD) on the central repo have a API parameter that indicate thetransaction ID (aka ‘branch ID’) to which the requested CRUD applies.

If an enterprise application integration (EAI) REST call is invokedwithout a transaction ID, the REST behavior is identical to that ofsingle-node. However, internally the global transaction ID generator isincremented if the call succeeds (this is necessary so that the/services/clusterstate endpoint's response, can still indicate thetransaction ID that each node is at).

In various embodiments, REST EAI actions can group together a sequenceinto a transaction. For example, the administrator could:

-   -   1. Create a transaction    -   2. create an index    -   3. create an input    -   4. create a source type    -   5. commit

If the administrator wants to schedule the deployment of the changes ata later time, the commit (Step 5) can be scheduled at some time in thefuture.

3.1. Example Processes of Operation

FIGS. 24-27 illustrate flow diagram 2400 of example processes, accordingto various embodiments. Procedures of these processes will be describedwith reference to elements and/or components described above. It isappreciated that in some embodiments, the procedures may be performed ina different order than described, that some of the described proceduresmay not be performed, and/or that one or more additional procedures tothose described may be performed. Flow diagram 2400 includes someprocedures that, in various embodiments, are carried out by one or moreprocessors under the control of computer-readable andcomputer-executable instructions that are stored on non-transitorycomputer-readable storage media. It is further appreciated that one ormore procedures described in flow diagram 2400 may be implemented inhardware, or a combination of hardware with firmware and/or software.

FIG. 24 illustrates a process 2400 for configuring a distributedcomputer system comprising a plurality of nodes of a plurality of nodeclasses, in accordance with some embodiments. In some embodiments, adata intake and query system (e.g., system 108) performs at least aportion of process 2400. Process 2400 starts by storing, at 2410,configuration files for a plurality of nodes of each of the plurality ofnode classes are stored in a central repository. The configuration filesinclude information representing a desired system state of thedistributed computer system, and the distributed computer systemoperates to keep an actual system state of the distributed computersystem consistent with the desired system state. the plurality of nodeclasses includes forwarder nodes for receiving data from an inputsource, indexer nodes for indexing the data, and search head nodes forsearching the data. In one embodiment, the central repository isconfigured to maintain a plurality of versions of the configurationfiles.

In one embodiment, the central repository includes a commonconfiguration file sub-repository for maintaining configuration filescommon to the plurality of nodes of the plurality of node classes, aforwarder configuration sub-repository for maintaining configurationfiles for the forwarder nodes, an indexer configuration sub-repositoryfor maintaining configuration files for the indexer nodes, and a searchhead configuration sub-repository for maintaining configuration filesfor the search head nodes. In one embodiment, configuration files forspecific nodes are stored in a particular portion of the centralrepository, the configuration files for the specific nodes allowing forarbitrary configuration changes to the specific nodes.

At 2420, responsive to receiving changes to the configuration files, thechanges are propagated to nodes of the plurality of nodes impacted bythe changes based on a node class of the nodes impacted by the changes.In one embodiment, responsive to receiving changes in the configurationfiles impacting the search nodes, the changes are propagated to thesearch nodes from the central repository. In one embodiment, responsiveto changes in the configuration files impacting the index nodes, thechanges are propagated to the index nodes from the central repository.In one embodiment, responsive to changes in the configuration filesimpacting the forwarder nodes, the changes are propagated to theforwarder nodes from the central repository.

In one embodiment, the changes to the configuration files are receivedfrom a single management node of the distributed computer system. In oneembodiment, the changes are made to the plurality of nodes via arepresentational state transfer (REST) call.

In another embodiment, the changes are propagated from the centralrepository to a mediator node of the node class impacted by the changes.The changes are then propagated from the mediator node to the nodesimpacted by the changes. In one embodiment, the changes are propagatedfrom the central repository to a search head captain node of the searchhead nodes. The changes are then propagated from the search head captainnode to the search head nodes impacted by the changes. In oneembodiment, the changes are propagated from the central repository to acluster master of the indexer nodes. The changes are then propagatedfrom the cluster master to the indexer nodes impacted by the changes.

In one embodiment, portions of the nodes of the plurality of nodesimpacted by the changes are reloaded. In one embodiment, responsive tochanges in the configuration files comprising a stanza change, a deltaof the stanza change is propagated to the nodes of the plurality ofnodes impacted by the changes.

In one embodiment, as shown at 2430, responsive to receiving changes inthe configuration files at a search node, the changes are propagated tothe central repository from the search node.

In another embodiment, process 2400 proceeds to 2505 of FIG. 25, where,asynchronous status notifications representative of an actual state ofthe nodes are received from the nodes. In one embodiment, as shown at2510, status information representative of the actual state of thedistributed computer system is provided. As shown at 2512, progressinformation representative of progress in achieving consistency with thedesired state of the distributed computer system is provided. In anotherembodiment, as shown at 2515, acceptance criteria are compared to theasynchronous status notifications. At 2520, it is determined whether theacceptance criteria have been satisfied based at least in part on theasynchronous status notifications.

In another embodiment, process 2400 proceeds to 2605 of FIG. 26, where atransaction comprising a change to the configuration files is received.In one embodiment, the transaction further comprises acceptance criteriafor the change to the configuration files. In one embodiment, as shownat 2610, it is determined whether the transaction conflicts with theconfiguration files. At 2615, responsive to the transaction notconflicting with the configuration files, the transaction is merged intothe configuration files such that the desired system state is updated.

In another embodiment, process 2400 proceeds to 2705 of FIG. 27, where afirst transaction comprising a first change to the configuration filesis received. At 2710, a second transaction comprising a second change tothe configuration files is received, wherein the second transaction isreceived subsequent to the first transaction. At 2715, the first changeis merged into the configuration files. At 2720, it is determinedwhether the first change conflicts with the second change. At 2725,responsive to the first change conflicting with the second change, thesecond change is not merged into the configuration files, and anotification that the second change has not been merged into theconfiguration files is provided.

Described herein are techniques for configuring components of adistributed system through a user interface. Components of distributedsystem (e.g., system 108 of FIG. 1) are configurable and may refer toconfiguration files stored locally at each component and/or stored in acentral repository (e.g., central repository 1812). These configurationfiles may involve some level of user configuration to accommodateparticular types of data a user desires to analyze and to account forother user preferences. Embodiments described herein provide a userinterface for configuring the components of a distributed system.

In various embodiments, a computer-implemented method is provided forconfiguring a distributed computer system comprising a plurality ofnodes of a plurality of node classes, a plurality of configurationcontrols enabling user configuration of the distributed computer systemare displayed via a graphical user interface of a computing devicehaving a display screen. The plurality of configuration controls includea forwarder configuration control for configuring forwarder nodes of aforwarder node class of the distributed computer system, an indexerconfiguration control for configuring indexer nodes of an indexer nodeclass of the distributed computer system, and a search headconfiguration control for configuring search head nodes of a search headnode class of the distributed computer system. In response to receivinga command for configuring a node of the distributed computer system atthe graphical user interface, a control node of the distributed computersystem configured for executing the command is identified. The commandis issued to the identified control node for execution.

With reference to FIG. 18A, administrator user interface 1805 includes agraphical user interface for providing configuration controls forconfiguring nodes of the distributed computer system 1800. In variousembodiments, the graphical user interface of administrator userinterface 1805 provides a unified point of management and control of theconfiguration of nodes of the distributed computer system 1800.

In various embodiments, the graphical user interface is configured topresent status information representative of the actual state of thedistributed computer system in response to receiving asynchronous statusnotifications from nodes. In other embodiments, the graphical userinterface is also configured to present progress informationrepresentative of the progress made in achieving consistency with thedesired state of the distributed computer system.

FIGS. 28A-28D illustrate a series of user interface screens for anexample node configuration user interface 2800, in accordance with thesome embodiments. As illustrated, node configuration user interface 2800shows an example node configuration for forwarder nodes. However, itshould be appreciated that nodes of any node class (e.g., forwardernodes, indexer nodes, or search head nodes) can be configured throughnode configuration user interface 2800.

FIG. 28A illustrates example node configuration user interface screen2810 comprising a list of nodes (or groups of nodes) 2812. Whileconfiguration user interface screen 2810 illustrates one group of nodes2812, it should be appreciated that there can be any number of nodes orgroups of nodes, and that the nodes or groups of nodes can be selectedfrom any node class. Control element 2814 provides a selectable controlfor creating a new node or group of nodes. Actions 2816 provideselectable commands for editing, configuring and deleting the selectednode or group of nodes. For example, as shown in FIG. 28A, serverclasses (e.g., groups of forwarder nodes) are illustrated forfacilitating collective management of a group of forwarder nodes.

FIG. 28B illustrates example node configuration user interface screen2820 comprising a node configuration window 2822. Node configurationwindow 2822 is displayed in response to a user selection of aconfiguration action 2816. Node configuration window 2822 provides aninterface for receiving edits to a configuration file or stanza within aconfiguration file. As shown, node configuration window 2822 includesselectable fields 2824a-c. It should be appreciated that nodeconfiguration window 2822 can include any number of selectable fields,and that the number and types of fields are dependent on the node classof the node or group of nodes being configured. For example, selectablefields 2824 a-c may include text boxes, drop-down lists, menus, listboxes, etc. As shown in FIG. 28B, node configuration window 2822includes selectable fields for configuring a stanza, including a stanzaname field 2824 a, a key field 2824 b, and a value field 2824 c.

FIG. 28C illustrates and example pending changes user interface screen2830 including a list of pending changes 2832 made to nodes. Interactingwith a pending change 2832, as shown in

FIG. 28D, displays a deployment selection window 2842, for receiving acommand to deploy the selected change.

FIG. 29 illustrates flow diagram 2900 of example processes, according tovarious embodiments. Procedures of these processes will be describedwith reference to elements and/or components described above. It isappreciated that in some embodiments, the procedures may be performed ina different order than described, that some of the described proceduresmay not be performed, and/or that one or more additional procedures tothose described may be performed. Flow diagram 2900 includes someprocedures that, in various embodiments, are carried out by one or moreprocessors under the control of computer-readable andcomputer-executable instructions that are stored on non-transitorycomputer-readable storage media. It is further appreciated that one ormore procedures described in flow diagram 2900 may be implemented inhardware, or a combination of hardware with firmware and/or software.

FIG. 29 illustrates a process 2900 for configuring a distributedcomputer system comprising a plurality of nodes of a plurality of nodeclasses, in accordance with some embodiments. In some embodiments, adata intake and query system (e.g., system 108) performs at least aportion of process 2900. Process 2900 starts by displaying, at 2910, aplurality of configuration controls enabling user configuration of thedistributed computer system, via a graphical user interface of acomputing device having a display screen. The plurality of configurationcontrols include a forwarder configuration control for configuringforwarder nodes of a forwarder node class of the distributed computersystem, an indexer configuration control for configuring indexer nodesof an indexer node class of the distributed computer system, and asearch head configuration control for configuring search head nodes of asearch head node class of the distributed computer system. In oneembodiment, as shown at 2920, the displaying the plurality ofconfiguration controls includes displaying a configuration status ofnodes of the distributed computer system.

At 2930, in response to receiving a command for configuring a node ofthe distributed computer system at the graphical user interface, acontrol node of the distributed computer system configured for executingthe command is identified. In one embodiment, the control node is amediator node of the node class. In one embodiment, the control node isa deployment server for the forwarder node class. In another embodiment,the control node is a cluster master for the indexer node class. Inanother embodiment, the control node is a coordinator node for theindexer node class. In another embodiment, the control node is a searchhead cluster deployer for the search head node class. In anotherembodiment, the control node is a captain node for the search head nodeclass. In various embodiments, in response to receiving a command forconfiguring a node of the distributed computer system at the graphicaluser interface, the configuration file for the node at the centralrepository is updated.

At 2940, the command is issued to the identified control node forexecution. In one embodiment, as shown at 2950, the command is executedat the identified control node. In one embodiment, as shown at 2960, aconfiguration status update from the node. At 2970, the configurationstatus of the graphical user interface is updated to display theconfiguration status update for the node.

The above description illustrates various embodiments of the presentinvention along with examples of how aspects of the present inventionmay be implemented. The above examples and embodiments should not bedeemed to be the only embodiments, and are presented to illustrate theflexibility and advantages of the present invention as defined by thefollowing claims. Based on the above disclosure and the followingclaims, other arrangements, embodiments, implementations and equivalentswill be evident to those skilled in the art and may be employed withoutdeparting from the spirit and scope of the invention as defined by theclaims.

What is claimed is:
 1. A computer-implemented method for configuring adistributed computer system comprising a plurality of nodes thatincludes at least a forwarder node, an indexer node, and a search headnode, the method comprising: receiving one or more configuration filesfor each of the forwarder node, the indexer, and the search head node;in response to receiving a first change to the one or more configurationfiles, which impacts the forwarder node, propagating the first change tothe one or more configuration files to the forwarder node; in responseto receiving a second change to the one or more configuration files,which impacts the indexer node, propagating the second change to the oneor more configuration files to the indexer node; and in response toreceiving a third change to the one or more configuration files, whichimpacts the search head node, propagating the third change to the one ormore configuration files to the search head node.
 2. Thecomputer-implemented method of claim 1, further comprising: receivingasynchronous status notifications representative of an actual state ofat least one of the plurality of nodes; providing status informationrepresentative of the actual state of the at least one of the pluralityof nodes; and providing progress information representative of progressin achieving consistency with a desired state of the distributedcomputer system, wherein at least one of the received first, second, orthird change to the one or more configuration files indicates thedesired state of the distributed computer system.
 3. Thecomputer-implemented method of claim 1, further comprising: receiving atransaction comprising that includes at least one of the first, second,or third changes to the one or more configuration files, wherein thetransaction further comprises acceptance criteria for the at least oneof the first, second, or third changes to the one or more configurationfiles.
 4. The computer-implemented method of claim 1 further comprising:receiving asynchronous status notifications from at least one of theplurality of nodes, wherein the asynchronous status notificationsindicate an actual state of the plurality of nodes; comparing acceptancecriteria to the asynchronous status notifications; and determiningwhether the acceptance criteria has been satisfied based at least inpart on the asynchronous status notifications.
 5. Thecomputer-implemented method of claim 1, further comprising: receiving atransaction comprising that includes at least one of the first, second,or third changes to the one or more configuration files; determiningwhether the transaction conflicts with the one or more configurationfiles; and responsive to the transaction not conflicting with the one ormore configuration files, merging the transaction into the one or moreconfiguration files such that the desired system state is updated. 6.The computer-implemented method of claim 1, further comprising:receiving a first transaction that includes the first change to the oneor more configuration files; receiving a second transaction thatincludes a foruth change to the one or more configuration files, whereinthe second transaction is received subsequent to the first transaction;merging the first change into the one or more configuration files;determining whether the first change conflicts with the fourth change;responsive to the first change conflicting with the fourth change, notmerging the fourth change into the configuration files; and providing anotification that the fourth change has not been merged into theconfiguration files.
 7. The computer-implemented method of claim 1,wherein the first, second, and third changes to the one or moreconfiguration files are received from a single management node of theplurality of nodes.
 8. The computer-implemented method of claim 1,further comprising: in response to receiving the first change to the oneor more configuration files, updating a configuration of the forwardernode.
 9. The computer-implemented method of claim 1, further comprising:in response to receiving the first change to the one or moreconfiguration files at the forwarder node, propagating the first changefrom the forwarder node to a central repository of the distributedcomputer system.
 10. The computer-implemented method of claim 1, furthercomprising: in response to receiving the second change to the one ormore configuration files, updating a configuration of the indexer node.11. The computer-implemented method of claim 1, further comprising: inresponse to receiving the second change to the one or more configurationfiles at the indexer node, propagating the second change from theindexer node to a central repository of the distributed computer system.12. The computer-implemented method of claim 1, further comprising: inresponse to receiving the third change to the one or more configurationfiles, updating a configuration of the indexer node.
 13. Thecomputer-implemented method of claim 1, further comprising: in responseto receiving the third change to the one or more configuration files atthe search head node, propagating the third change from the search headnode to a central repository of the distributed computer system.
 14. Thecomputer-implemented method of claim 1, wherein a central repository ofthe distributed computer system is configured to maintain a plurality ofversions of the one or more configuration files.
 15. Thecomputer-implemented method of claim 1, wherein the first, second, andthird changes are made to the plurality of nodes via a representationalstate transfer (REST) call.
 16. The computer-implemented method of claim1, further comprising: receiving the third changes at the search headnode; employing the search head node to pull at least one of the one ormore configuration files from a search head captain node of theplurality of nodes; merging the third changes with the at least onepulled configuration file; pushing the at least one pulled configurationfile with the third changes to the search head captain node, wherein theat least one pulled configuration file is updated to include the thirdchanges; and pushing the at least one pulled configuration file to acentral repository. Of the distributed computer system.
 17. Thecomputer-implemented method of claim 1, wherein the distributed computersystems includes a central repository that comprises: a commonconfiguration file sub-repository for maintaining a first configurationfile of the one or more configuration files, wherein the commonconfiguration file sub-repository is common to the plurality of nodes; aforwarder configuration sub-repository for maintaining a secondconfiguration file of the one or more configuration files for theforwarder node; an indexer configuration sub-repository for maintaininga third configuration file of the one or more configuration files forthe indexer node; and a search head configuration sub-repository formaintaining a fourth configuration file of the one or more configurationfiles for the search head node.
 18. The computer-implemented method ofclaim 1, further comprising: propagating the first, second, and thirdchanges from a central repository of the distributed computer system toa mediator node of the plurality of nodes; and propagating the first,second, and third changes from the mediator node to the forwarder,indexer, and search head nodes.
 19. The computer-implemented method ofclaim 1, further comprising: propagating the third changes from acentral repository of the distributed computer system to a search headcaptain node of the plurality of nodes; and propagating the thirdchanges from the search head captain node to the search head node. 20.The computer-implemented method of claim 1, further comprising:propagating the second changes from a central repository of thedistributed computer system to a cluster master of the plurality ofnodes; and propagating the second changes from the cluster master to theindexer node.
 21. The computer-implemented method of claim 1, furthercomprising: reloading each of the forwarder, indexer, and search headnodes.
 22. The computer-implemented method of claim 1, furthercomprising: storing the one or more configuration files in a centralrepository of the distributed computer system.
 23. Thecomputer-implemented method of claim 1, further comprising: storing afirst configuration file of the one or more configuration files in afirst portion of a central repository of the distributed computingsystem, wherein the first configuration file includes the first changeand the first portion of the central repository is associated with theforwarder node; storing a second configuration file of the one or moreconfiguration files in a second portion of the central repository,wherein the second configuration file includes the second change and thesecond portion of the central repository is associated with the indexernode; and storing a third configuration file of the one or moreconfiguration files in a third portion of the central repository,wherein the third configuration file includes the third change and thethird portion of the central repository is associated with the searchhead node.
 24. A non-transitory computer-readable storage mediumcontaining instructions which when executed on one or more dataprocessors, cause the one or more data processors to perform operationsfor configuring a distributed computer system comprising a plurality ofnodes that includes at least a forwarder node, an indexer node, and asearch head node, the operations comprising: receiving one or moreconfiguration files for each of the forwarder node, the indexer, and thesearch head node; in response to receiving a first change to the one ormore configuration files, which impacts the forwarder node, propagatingthe first change to the one or more configuration files to the forwardernode; in response to receiving a second change to the one or moreconfiguration files, which impacts the indexer node, propagating thesecond change to the one or more configuration files to the indexernode; and in response to receiving a third change to the one or moreconfiguration files, which impacts the search head node, propagating thethird change to the one or more configuration files to the search headnode.
 25. The non-transitory computer-readable storage medium of claim24, wherein the operations further comprise: receiving asynchronousstatus notifications representative of an actual state of at least oneof the plurality of nodes; providing status information representativeof the actual state of the at least one of the plurality of nodes; andproviding progress information representative of progress in achievingconsistency with a desired state of the distributed computer system,wherein at least one of the received first, second, or third change tothe one or more configuration files indicates the desired state of thedistributed computer system.
 26. The non-transitory computer-readablestorage medium of claim 24, wherein the operations further comprise:receiving asynchronous status notifications from at least one of theplurality of nodes, wherein the asynchronous status notificationsindicate an actual state of the plurality of nodes; comparing acceptancecriteria to the asynchronous status notifications; and determiningwhether the acceptance criteria has been satisfied based at least inpart on the asynchronous status notifications.
 27. The non-transitorycomputer-readable storage medium of claim 24, wherein the operationsfurther comprise: receiving a transaction comprising that includes atleast one of the first, second, or third changes to the one or moreconfiguration files; determining whether the transaction conflicts withthe one or more configuration files; and responsive to the transactionnot conflicting with the one or more configuration files, merging thetransaction into the one or more configuration files such that thedesired system state is updated.
 28. The non-transitorycomputer-readable storage medium of claim 24, wherein the operationsfurther comprise: receiving a first transaction that includes the firstchange to the one or more configuration files; receiving a secondtransaction that includes a foruth change to the one or moreconfiguration files, wherein the second transaction is receivedsubsequent to the first transaction; merging the first change into theone or more configuration files; determining whether the first changeconflicts with the fourth change; responsive to the first changeconflicting with the fourth change, not merging the fourth change intothe configuration files; and providing a notification that the fourthchange has not been merged into the configuration files.
 29. A systemcomprising: one or more data processors; and one or morecomputer-readable storage media containing instructions which whenexecuted on the one or more data processors, cause the one or moreprocessors to perform operations for configuring a distributed computersystem comprising a plurality of nodes that includes at least aforwarder node, an indexer node, and a search head node, the operationscomprising: receiving one or more configuration files for each of theforwarder node, the indexer, and the search head node; in response toreceiving a first change to the one or more configuration files, whichimpacts the forwarder node, propagating the first change to the one ormore configuration files to the forwarder node; in response to receivinga second change to the one or more configuration files, which impactsthe indexer node, propagating the second change to the one or moreconfiguration files to the indexer node; and in response to receiving athird change to the one or more configuration files, which impacts thesearch head node, propagating the third change to the one or moreconfiguration files to the search head node.
 30. The system of claim 29,the operations further comprising: receiving asynchronous statusnotifications representative of an actual state of at least one of theplurality of nodes; providing status information representative of theactual state of the at least one of the plurality of nodes; andproviding progress information representative of progress in achievingconsistency with a desired state of the distributed computer system,wherein at least one of the received first, second, or third change tothe one or more configuration files indicates the desired state of thedistributed computer system.